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Welcome to the TalentSeeker Blog

You'll find the latest HR trends, effective talent management tips, and practical insights on how to get the most out of TalentSeeker's solutions—all designed to help you build smarter HR strategies.
We’re committed to filling this space with valuable resources to support your business growth every step of the way!
The greatest danger in times of turbulence is not the turbulence itself, but to act with yesterday's logic. - Peter Drucker
HR leaders must take on the role of true "risk managers" amidst economic uncertainty, rapidly changing market environments, and technological advancements like artificial intelligence (AI). According to an HR trends report, 64% of executives believe their organizations can withstand unforeseen shocks, but this is only achievable when risk management is integrated with people management.

Psychological Safety Determines Organizational Sustainability

"Psychological safety" refers to an environment where employees feel free to express their opinions without fear of making mistakes. It’s similar to how drivers feel more secure simply by knowing they are wearing a seatbelt on a slippery road.
According to an HR report, 57% of employees are working longer hours than before, with many experiencing high levels of stress. HR leaders must improve corporate culture to ensure employees feel psychologically safe in such circumstances.
Simply offering benefits is not enough. Organizations must create an environment where employees can voice their opinions, work autonomously, and embrace mistakes without fear. For example, Google’s Project Aristotle found that teams with high psychological safety outperform others in both innovation and productivity.

Risk Management Through a Healthy Corporate Culture

A healthy corporate culture is not just about having a "good atmosphere." It means creating an environment where employees can maintain their mental and physical well-being.
According to Mercer, 82% of employees feel at risk of burnout, which not only reduces productivity but also increases organizational risks.
Risk Management Through Building a Healthy Corporate Culture
Recruiting Management

New Challenges and Opportunities for Global Business

In today’s world, expanding into the global market is a critical strategy for gaining a competitive advantage. Especially for small and medium-sized enterprises (SMEs), limited resources and experience present many challenges when building and managing a global team.
Companies preparing for global expansion need to forecast future business needs and build resilient teams using flexible solutions. This article presents scalable business strategies for successfully managing global teams, from talent acquisition to crisis management.

1. Building a Scalable Talent Acquisition Strategy

Scalable workforce planning refers to the ability to flexibly expand talent acquisition and management systems to match the changing business environment. This involves not just hiring new employees but also addressing cultural fit, language proficiency, and utilizing flexible contract types based on project requirements.

Key Talent Acquisition Strategies

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Leverage Global Recruitment Channels: Actively utilize not only LinkedIn but also localized job platforms.
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Assess and Verify Fit: Implement structured interviews, skills assessments, and AI-driven assessment automation.
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Sustainable Talent Management: Offer reskilling and upskilling programs to respond to rapid growth.
How to Successfully Build a Global Team
Recruiting Management

Introduction

With the advancement of artificial intelligence (AI) technology, there has been a growing interest in building models optimized for specific tasks. In a previous article, we explored how to apply pre-trained models to specific domains using Transfer Learning. In this article, we will focus on a technique called Instruction Tuning, which fine-tunes AI models to respond more effectively to given instructions. This is a different approach from traditional Fine-Tuning, and it plays a critical role in enhancing the adaptability and efficiency of AI systems.

What is Instruction Tuning?

Instruction Tuning is a technique that enables AI models to understand and perform natural language instructions. While Fine-Tuning adjusts a model by using large amounts of data for a specific task, Instruction Tuning involves providing natural language instructions and corresponding example data to help the model perform tasks more flexibly and intuitively.
This method was proposed in the FLAN (Finetuned Language Models Are Zero-Shot Learners) paper, and its main goal is to improve the generalization performance of the model by providing explicit instructions for various tasks. For example, instead of just completing a sentence, a model might learn to understand and execute an instruction like "Summarize this sentence in one line."

How Instruction Tuning Works

Instruction Tuning is applied to large language models (LLMs) through the following steps:
1.
Data Collection and Preprocessing: A dataset is created containing commands and corresponding input-output pairs for various tasks.
2.
Model Training: The Instruction Tuning dataset is added to a pre-trained language model (e.g., T5, GPT, BERT) for further training.
3.
Evaluation and Generalization Testing: The model’s zero-shot and few-shot performance is tested on various tasks to assess its generalization ability.
Instruction Tuning: More Flexibility with Less Data
HR Tech

The First 44 Days: How Company Culture Decides Everything

While hiring top talent is crucial, what matters even more is ensuring these individuals stay with the organization. New employees typically assess whether the company is a good fit for them within their first 44 days. If their experience during this time isn't satisfactory, they are likely to consider leaving.
So, what are the factors that determine whether an employee stays or leaves? The answer is a strong company culture.

Why Company Culture Matters

Today, employees don’t just choose companies based on salary and benefits. Most employees consider whether the company's values and vision align with their own goals and values.
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94% of employees say that feeling a sense of belonging at work is important (Source: American Psychological Association)
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Organizations with positive cultures have employee retention rates that are 30% higher (Source: Gallup)
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Companies with a healthy culture have employee engagement levels that are 3.8 times higher (Source: Forbes)
In other words, the stronger the company culture, the more employees feel a sense of belonging, and the greater their long-term commitment to the company. One of the most important factors in building a strong company culture is employee recognition.

Employee Recognition Builds a Strong Company Culture

What is Employee Recognition?

Powerful Strategies to Increase Employee Retentio
communication strategy
Hello, I'm woosik Na, the CEO of TalentSeeker.
At TalentSeeker, we are continuously updating features to improve the inefficiencies in the hiring process based on feedback from recruiters.
In our previous update, we introduced AI-powered talent search and communication optimization features under the theme “Faster and More Accurate with Large Language AI.”
In this update, we present the new ‘Communication Channel Integration’ feature to help recruiters overcome the hassle of switching between multiple platforms.
By integrating email and LinkedIn, we allow recruiters to handle all communications in one place and communicate with candidates in real-time.

The Inefficiency of Traditional Hiring Communication

Recruiters often need to use multiple communication channels during the hiring process. However, these platforms are often disconnected, making communication cumbersome and difficult to manage.
These are common concerns shared by recruiters in both startups and large corporations.
Communication Channel Integration: Email/LinkedIn Integration
Update Story

HR: From Simple Management to Strategic Business Partner

As an HR professional, how strategically are you working?
In the past, HR was seen as a support function focused mainly on recruitment and personnel management. However, HR has now become a core growth driver for organizations. With increasing economic uncertainty and a greater focus on leveraging internal talent and improving performance rather than just recruitment, the role of HR has evolved from mere operational management to becoming a Strategic Business Partner.
What if hiring slows down? How can existing talent be developed?
What if performance stagnates? What are the practical ways to improve employee performance?
What if data is leaked? How can confidentiality and security issues be managed?
Now, HR is responsible for both performance management and data protection, driving the organization’s ongoing growth and innovation. So, how can HR play this strategic role effectively?
This article will explore how HR can enhance its strategic role in organizations by focusing on Performance Management and Data Confidentiality.

Performance Management: How HR Drives Organizational Growth

Why is Performance Management Important?

The Evolving Role of HR: From Performance Management to Data Protection
HR Strategy

Why Salary Negotiations and Onboarding Matter in Employee Retention Strategies

Employee retention is not just about offering a competitive salary or increasing benefits. Fair salary negotiations from the start and a structured onboarding process are key elements that determine long-term employee satisfaction and retention.
According to recent research from BambooHR, 50% of employees are struggling to make ends meet, and 73% are asking for a salary increase in their current roles. Furthermore, 70% of new hires decide whether the organization is a good fit within the first month, and 44% regret their decision within the first week.
These statistics make it clear how salary negotiations and onboarding play a crucial role in employee retention. When employees feel that they are receiving a fair salary and are treated equitably, and when they successfully adapt to the company culture in the early stages of their employment, the likelihood of long-term retention increases.
In this article, we'll provide 5 salary negotiation tips and 5 onboarding goals that HR professionals can implement to improve employee retention.

Salary Negotiation: The First Step in Employee Retention

Salary negotiation is not just about setting a salary. It’s an important moment to communicate how valued the employee is within the organization. If salary negotiations are not handled properly, employees may lose trust in the organization and be more likely to seek better opportunities.

1. Total Compensation Package

Salary negotiation isn’t just about the base salary. Employees consider the total compensation package, and HR professionals must effectively communicate this to them. Before starting salary negotiations, HR professionals should understand the total compensation package the organization can offer and be ready to explain it.
Compensation Elements to Consider:
Flexible Work Arrangements: Offer remote work or hybrid work options.
How to Get the First Step Right After Hiring: A Guide to Salary Negotiation and Onboarding
HR Strategy
Hello, this is woosik NA, the CEO of TalentSeeker.
At TalentSeeker, we continuously update our features based on customer feedback. In the previous update, we introduced AI-powered talent discovery and communication optimization tools under the theme "Faster and More Accurate with Large Language AI."
In this post, we are excited to introduce one of the key features from the previous update: Personalized Candidate Email Sequences. We will explore how this feature differs from traditional recruitment methods and how it can revolutionize communication in real-world hiring processes.

Limitations of Traditional Recruitment Emails

Through conversations with countless recruiters, we’ve heard about the difficulties they face in writing and managing recruitment emails.
"I need to write different emails for each candidate, but it takes all day."
"When candidates don’t respond, I’m unsure if I should follow up or just give up."
"I’ve emailed the same candidate before, but can I send another one?"
These challenges are common across all industries and company sizes. Recruitment email inefficiency is a pain point for all recruiters, whether they work at startups, IT companies, or large corporations.

Writing Custom Messages for Each Candidate Takes a Lot of Time

Personalized Candidate Email Sequences.
Update Story
Hello, this is woosik NA, CEO of TalentSeeker.
At TalentSeeker, we continue to make regular updates every month based on feedback from our customers. In our last update, we introduced AI-driven features to help hiring managers discover candidates faster and communicate more efficiently under the theme “Faster and More Accurate with Large Language AI.”
In this article, we will dive into one of the major features from the last update, Conversational Talent Search (TalentGPT), and explore how this technology is different from traditional hiring methods and what changes it can bring to the hiring process.

Limitations of Traditional Talent Search Methods

The recruitment market is rapidly evolving, yet traditional talent search methods still face many limitations. After interviewing numerous hiring managers, we identified the key issues with existing talent search methods.
Challenges such as inefficient search processes, superficial keyword matching, difficulty in fine-tuned searches, and the lack of incorporation of the latest technological trends have led hiring managers to spend significant time and effort to find the best candidates.

Complex Filters: Inefficient Search Process

In the traditional search method, hiring managers typically combine multiple filters to find the desired candidates. However, this manual search process has several limitations:
Fatigue from Repetitive Filter Adjustments
Talent Search Starting with Conversation (TalentGPT)
Update Story

Introduction

Large Language Models (LLMs) are driving innovative changes in the field of Natural Language Processing (NLP). Models like GPT-4, BERT, and T5, with billions of parameters, learn language structures and meanings from vast datasets, enabling them to understand context like humans and respond to complex queries.
The success of these models is based on two pillars: pre-training and transfer learning. However, training an LLM from scratch for every task is impractical due to the massive amounts of data, computational resources, and time required. This is where Transfer Learning becomes crucial, as it allows leveraging knowledge from a pre-trained model and applying it to new domains or tasks, greatly enhancing efficiency.
This article explores the core principles and theoretical background of Transfer Learning and how it can be applied in LLMs.

The Concept and Approach of Transfer Learning

Transfer Learning is a learning technique where knowledge acquired from one task is transferred to another. It is particularly useful in large language models. Typically, Transfer Learning involves two stages: pre-training and fine-tuning.
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Pre-training: In this phase, large language models are trained on vast, generic data to perform language modeling. They learn the general patterns of various languages, grammar, common-sense knowledge, and semantic relationships. For example, models like BERT and GPT learn to understand the relationships between words in sentences by analyzing large amounts of text data.
•
Fine-tuning: After pre-training, the model is further trained on specific data related to a particular task or domain. In this phase, fine-tuning optimizes the model’s performance for specialized tasks, like predicting medical diseases or analyzing documents.
This approach greatly enhances data efficiency and time efficiency. By using pre-trained models, the time and resources required for training are significantly reduced compared to training a model from scratch.
Transfer Learning: How to Leverage LLMs More Efficiently
HR Tech
Hello! This is the TalentSeeker Operations Team
The Weekly Case Study is a session where we analyze the activity logs of TalentSeeker users from the past week to help address challenges faced in the recruitment process. Using actual case studies based on publicly available job postings, we aim to provide practical assistance for your hiring journey.
Do you have any recruitment challenges you’d like to solve? If you’d like to discuss your concerns with us, feel free to reach out anytime via our KakaoTalk channel. Your valuable feedback helps us create better services.

Hiring a LLM Agent Engineer

This week’s case study is titled "Hiring a Data Engineer Lead at Socar using TalentSeeker".
Hiring for data engineer (lead) positions can be difficult due to the need for specialized skills, experience, complex technical requirements, leadership capabilities, and the increasing competition in the market. This role requires expertise in data pipeline construction, large-scale data processing, cloud technologies, and data analysis, as well as business domain knowledge. As a lead, the candidate must also have team leadership, decision-making abilities, and project management skills. Additionally, the growing demand for AI and big data projects has made it even more challenging to find the right candidates.
In this case study, we’ll introduce how to effectively find the perfect candidate for your job posting using TalentSeeker's TalentGPT. By simply copying and pasting your JD, the AI can quickly narrow down the ideal candidates without needing to manually adjust filters.
How to Hire a Data Engineer (Lead) with TalentSeeker: A Case Study of Socar
Case Study

The Impact of AI on Recruitment

Artificial Intelligence (AI) is playing an increasingly important role in recruitment teams by providing efficiency, personalization, and data-driven decision-making. According to a survey by SHRM, about 88% of global companies are already using AI in their HR processes.
AI goes beyond just technology adoption; it is revolutionizing the entire recruitment process from talent sourcing to hiring and retention. AI-based recruitment solutions automate repetitive tasks, saving time and enabling recruitment professionals to focus on more strategic aspects of their roles.
However, discussions on how to effectively utilize AI in recruitment are still ongoing, with HR experts holding different views.
At TalentSeeker, we aim to clarify the role of AI in recruitment activities and help HR and TA teams understand the positive impact AI can bring. This article is designed to provide insights into how AI is transforming the recruitment process.

What AI Recruitment Means

AI recruitment involves automating repetitive, time-consuming tasks and applying personalization and data insights throughout the recruitment process. For example, AI quickly processes vast amounts of data to improve efficiency and accuracy, ultimately enhancing organizational productivity.
AI offers automation capabilities that can quickly and effectively solve large-scale problems, such as screening, sourcing, and scheduling tasks. It is especially useful for handling repetitive tasks, allowing recruiters to focus on high-value initiatives.
While some worry that AI might replace human recruiters, this is not the case. AI is designed as a tool to assist talent acquisition teams, and it will continue to evolve in this direction.

Key Impacts of AI on Recruitment

2025 AI Recruitment Guide
Recruiting Management
Hello! This is woosik NA, CEO of TalentSeeker.
At TalentSeeker, we are continuously improving the recruitment process based on customer feedback through regular monthly updates.
This update focuses on using AI technology to help recruiters discover talent faster and communicate more efficiently, under the theme “Faster and More Accurate with Large Language AI.”
With new features such as JD-based candidate search, personalized candidate email creation, and integration with email and LinkedIn, we aim to revolutionize the recruitment process by saving time, ensuring effective communication, and providing accurate matching for busy recruiters.
Let’s dive into the new key features and the changes they can bring!

Talent Search with a Conversation (TalentGPT)

The feature that helps you find the right candidates faster significantly reduces the time spent during the recruitment process.
TalentSeeker’s JD-based candidate search has now evolved further. With a GPT-style conversational interface and the attachment upload feature, recruiters can easily provide job requirements, and AI will analyze the JD and recommend the best candidates.
This feature drastically reduces the time spent during the recruitment process. By using AI technology, we can compare candidates’ actual experience and skills against the JD and evaluate fit. The extensive global talent database allows us to uncover hidden top talent.
Faster and More Accurate with Large Language AI
Update Story

Introduction

The rapid advancement of artificial intelligence (AI) has brought revolutionary changes to the field of natural language processing (NLP). In particular, the evolution of language models has significantly improved how machines understand and generate text. Early on, statistical language models (SLM) were developed to learn language patterns and predict text based on probabilities. However, these models struggled with understanding long contexts and capturing complex linguistic nuances.
To overcome these limitations, neural language models (NLM) were introduced. These models learned words using distributed representations, allowing them to incorporate context and dramatically enhance language comprehension. The introduction of the Attention mechanism and the Transformer architecture further strengthened language models, paving the way for the development of pre-trained models (PLMs) such as BERT and GPT. Eventually, large language models (LLMs) emerged, marking a significant turning point in NLP.
Today’s LLMs leverage billions of parameters to excel in various tasks, including text generation, question-answering, and machine translation. Technologies like zero-shot learning and feedback-based learning have maximized their adaptability to new tasks, leading to applications in customer service automation, data analysis, and content creation across multiple industries.
This wave of innovation has also significantly impacted the recruitment industry. TalentSeeker utilizes LLM technology to overcome the limitations of traditional hiring processes, revolutionizing how companies connect with talent. For instance, TalentSeeker recommends candidates based on actual work experience and skills rather than just resumes, while also automating complex back-office tasks to maximize efficiency.
In this article, we will explore the evolution of language models in four key stages: Statistical Models, Neural Language Models, Pre-trained Models, and Large Language Models. We will examine the technological breakthroughs at each stage and how they have overcome the challenges in NLP. Additionally, we will highlight how TalentSeeker is leveraging LLMs to reshape the recruitment industry and unlock new possibilities.

The Evolution of Language Models

This section covers the major milestones in the evolution of language models, from Statistical Language Models (SLMs) to Large Language Models (LLMs). Language models play a crucial role in NLP, enabling machines to understand and predict text. Initially, statistical models were used to learn language patterns, but as they struggled with complexity and scalability, neural language models emerged, bringing more refined and flexible approaches. The advent of pre-trained models (PLMs) and LLMs has since led to remarkable improvements in NLP. Here, we will follow the progression of these models and explore how they have enhanced language comprehension and prediction.

Statistical Language Models (SLM)

From Statistical Models to Large Language Models: The Paradigm Shift in NLP
HR Tech
Hello! This is the TalentSeeker Operations Team
Our Weekly Case Study analyzes user activity logs from the past week to identify and address challenges in the hiring process. By examining real cases based on public job postings, we aim to provide practical insights that can support your recruitment journey.
Do you have a hiring case you need help with? If you’d like to discuss a specific recruitment challenge, feel free to reach out to us via our KakaoTalk channel. Your valuable feedback helps us improve our services to better support your needs!

Hiring an LLM Agent Engineer

This week’s case study focuses on "Hiring an LLM Agent Engineer."
This role requires expertise in two highly specialized fields—Large Language Models (LLMs) and cryptocurrency—demanding a unique combination of advanced technical skills and domain knowledge. Candidates must have experience in LLM-specific development, including RAG (Retrieval-Augmented Generation) and fine-tuning, proficiency with frameworks like LangChain, a deep understanding of the cryptocurrency ecosystem, and the ability to work with specialized programming languages such as Rust. The challenge lies in sourcing talent from an extremely narrow candidate pool.
Using TalentSeeker’s advanced filtering features, we will demonstrate how to efficiently find the right talent for this position. By setting detailed criteria such as job role, responsibilities, and required technologies, recruiters can quickly refine their search and identify the best candidates faster and more effectively.
Stay tuned for insights on how to optimize your hiring process using TalentSeeker!
Hiring an LLM Agent Engineer with TalentSeeker: The Hyperithm Case Study
Case Study
Hello, this is Woosik NA, CEO of TalentSeeker.
At TalentSeeker, we incorporate customer feedback into our monthly updates to provide innovative tools that simplify recruitment complexity and enhance candidate relationship management. In our previous update, we introduced AI-powered headhunting technology, significantly improving recruitment efficiency.
In this article, we will introduce how the Unified Dashboard helps recruiters systematically manage candidate relationships and streamline the hiring process.

Listening to Customer Feedback

Through extensive customer interviews and data analysis, we identified the key challenges faced by recruiters. The following concerns were commonly raised:
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"It’s difficult to track where candidates are in the hiring process."
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"I can’t always remember past interactions with candidates or whether I’ve engaged with them in previous hiring projects."
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"I need a structured way to manage my communication and notes for each candidate."
Unified Dashboard: Streamline Candidate Communication at Every Stage
Update Story
Software engineers play a crucial role in designing, developing, and maintaining software systems, contributing to an organization's technological innovation and operational efficiency. To successfully hire top software engineers, it’s essential to have a clear understanding of their responsibilities, required skills, and qualifications.

The Role and Contributions of a Software Engineer

Software engineers take on key responsibilities that drive organizational success, including:
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Enhancing platform and network functionality: Optimizing existing platforms and networks to support business growth.
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Developing innovative software platforms: Creating new software platforms as product usability and visibility expand.
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Building scalable software and networking solutions: Utilizing unit testing, code reviews, and continuous integration to develop scalable, efficient solutions.
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Analyzing and improving system performance: Identifying system flaws, implementing monitoring tools, and developing automated recovery processes to maximize uptime.
•
Planning and executing the software development lifecycle: Aligning development projects with company standards and expectations to ensure high-quality outcomes.
[Job Position Guide] Software Engineer
Job Position Guide
Hello! This is the TalentSeeker Operations Team
Our Weekly Case Study analyzes TalentSeeker user activity logs from the past week to identify and address challenges in the hiring process. By examining real cases based on public job postings, we aim to provide practical insights to support your recruitment journey.
Do you have a hiring case you need help with? If you’d like to discuss a specific recruitment challenge, feel free to reach out to us via our KakaoTalk channel. Your valuable feedback helps us improve our services to better support your needs!

Hiring a Web-Front Engineer for Operations & Back Office

This week’s case study focuses on “Hiring a Web-Front Engineer for Operations & Back Office.”
This role requires a deep understanding of modern front-end technologies such as React and TypeScript, advanced experience with Framer-motion, expertise in UI/UX improvement and legacy refactoring, and familiarity with the mobility industry. Given the high technical specialization, industry-specific experience, and problem-solving skills required, the talent pool for this position is highly limited, making recruitment challenging.
We will demonstrate how to efficiently find the right talent for this role using TalentSeeker’s advanced filtering features. By setting detailed criteria such as job role, responsibilities, and required technologies, recruiters can narrow down and identify the best candidates quickly and effectively.
Hiring a Web-Front Engineer for Operations & Back Office with TalentSeeker: The SWING Case Study
Case Study

Generational and Environmental Shifts in the AI Era

With the rise of AI technology, Gen Z and Alpha generations are experiencing new ways of learning, communicating, and engaging in leisure activities. (Source: NIA) These AI-native generations prefer visual and video content over text and consider digital literacy as a key competitive advantage. This shift has a profound impact on corporate hiring and workplace dynamics.
CES 2025 is themed "Dive In," emphasizing the importance of connectivity, problem-solving, and exploring new possibilities through technology. AI and other cutting-edge technologies are no longer just tools; they are becoming the foundation for solving global challenges and building a sustainable future. Companies are leveraging these advancements to foster creativity, drive transformation, and connect people and industries in unprecedented ways.

The Innovations and Challenges Brought by AI

AI is not just a tool but a transformational force across industries such as media, finance, and healthcare. According to a Samjong KPMG report, AI adoption is driving the following changes:
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Productivity Gains: AI is projected to increase U.S. labor productivity by 1.5%.
•
Economic Impact: AI is expected to boost global GDP by 7%, generating $4.4 trillion in economic value.
Concerns About AI Adoption and the Role of Leaders
communication strategy
Hello, this is woosik NA, CEO of TalentSeeker.
At TalentSeeker, we actively integrate customer feedback into our monthly updates to drive innovation that simplifies recruitment complexity and enhances efficiency. In our previous update, we introduced AI-powered headhunting technology, taking hiring efficiency to the next level.
In this article, we will explore one of the key features from the latest update—Candidate Screening—and demonstrate how recruiters can save time and quickly identify the right talent.

Recognizing the Time-Consuming Challenges for Recruiters

Recruiters typically review over 1,000 candidates per position. Even if just 3 minutes are spent on each candidate, this adds up to a staggering 50 hours (3,000 minutes) of screening time.
There are two primary reasons why this process is so time-intensive:

Inconsistent resume formats

•
Candidates submit resumes through multiple platforms, each with different structures for work experience, responsibilities, and education.
Discover Faster, Candidate Screening
Update Story

Introduction

Clustering is one of the oldest techniques in AI and data analysis, and it has been widely used across various fields for decades. This longevity proves that clustering is not just a fundamental but also a powerful tool. In particular, in HR fields such as job skillset analysis, clustering remains an effective method for simplifying data and deriving clear insights based on similarities.
Peter Norvig, an AI researcher at Stanford University, once said:
"Simplicity is not the opposite of power. Sometimes, the simplest algorithms yield the most profound insights."
Clustering exemplifies this principle by leveraging simplicity for powerful results. For instance, the K-means algorithm, first introduced in the 1950s, remains one of the most fundamental and powerful tools for understanding the structural characteristics of data. It continues to be an essential method for deriving insights in HR, marketing, healthcare analytics, and many other fields.
Particularly in HR, clustering is highly effective in identifying similarities and differences in job skillsets. It helps in defining job requirements more precisely, identifying the most suitable candidates, and optimizing hiring strategies.
Clustering is not only an established technique but also a high-potential method when combined with modern AI and machine learning technologies. In this article, we will explore how clustering techniques can be effectively utilized for analyzing job skillsets.

What is Clustering?

Clustering is an unsupervised learning technique that groups similar data points together based on their similarities. It calculates how similar each data point is to another and categorizes them into meaningful clusters. Since clustering does not require labeled data, it is highly useful for analyzing the similarities between required skillsets across different job roles.
For example, clustering can help identify which technical skills are required for data science, software development, and marketing roles. HR teams can then use this insight to recommend the most suitable candidates for each job.
Simple Yet Powerful Clustering for Job Skillset Analysis
HR Tech
Hello, this is woosik NA, CEO of TalentSeeker. TalentSeeker incorporates customer feedback into monthly updates to continuously enhance our platform. In our previous update, we introduced AI-driven recruitment tools that capture the core expertise of headhunters, further transforming the hiring process.
In this article, we will explore one of the key features from the last update—Task-Based Search—and discuss how it helps solve recruitment challenges.

Recognizing the Challenges of Task-Based Assessment

Effective recruitment goes beyond resume screening—it requires evaluating candidates based on the tasks they have performed and the results they have achieved. Studies and reports suggest that task-based evaluation is one of the most reliable predictors of job performance, which is why headhunters prioritize this approach.
However, implementing this assessment method has proven challenging in real-world scenarios. Some of the most common difficulties include:
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Difficulty in assessing unstructured tasks: Evaluating non-standardized data such as free-form project descriptions and work records can be complex.
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Lack of consistency: Subjective evaluation criteria lead to inconsistent assessments across candidates.
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Time-consuming validation: Reviewing a candidate’s actual work performance requires extensive time and effort.
To address these issues, TalentSeeker has integrated AI-driven verification methods based on the expertise of top headhunters, delivering accurate and reliable results.
Search from Over 500 Task Categories
Update Story
Hello! This is the TalentSeeker Operations Team
We’re excited to introduce our Weekly Case Study, a new initiative designed to help recruiters overcome hiring challenges by analyzing real-world job postings and user activity logs.
Each week, we examine TalentSeeker user activity logs to identify key pain points in the hiring process and provide actionable insights.
Do you have a hiring case you'd like us to explore? Feel free to reach out via our KakaoTalk channel, and let’s find solutions together! Your valuable feedback helps us improve our services and enhance the hiring experience.

Hiring a Personalized Recommendation Product Manager

This week’s case study focuses on “Hiring a Personalized Recommendation PM.”
This role requires data-driven problem-solving, designing personalized recommendation models, and cross-functional collaboration—a highly complex and demanding skill set. Additionally, candidates must have 8+ years of experience, managing the entire product lifecycle while understanding business strategy. Finding the right talent for such a specialized role can be extremely challenging due to the limited supply of candidates who meet these criteria.
We’ll demonstrate how to efficiently identify and connect with top candidates using TalentSeeker’s advanced filtering tools. By applying filters for job roles, key responsibilities, and technical expertise, you can streamline your hiring process and discover top talent faster.
Hiring a Personalized Recommendation Product Manager with TalentSeeker: A Case Study Featuring Yanolja
Case Study

Introduction

In modern hiring platforms, effectively leveraging vast amounts of data while providing personalized results is becoming increasingly important. Traditional search engines and AI models often face limitations, which is where Retrieval-Augmented Generation (RAG) comes in.
This article explores the concept of RAG, how it can be used to build an AI-powered hiring system, and provides a hands-on example using Python and OpenAI’s GPT.

What is Retrieval-Augmented Generation (RAG)?

Retrieval-Augmented Generation (RAG) is a novel natural language processing (NLP) technique designed to overcome the limitations of large language models (LLMs). It enhances text generation accuracy by retrieving relevant external data in real-time and using that information to generate responses.

Why RAG?

Traditional language models generate text based solely on their pre-trained datasets. Since these models rely on static data, they struggle to provide accurate responses about recent events or newly emerging knowledge.
For example, an LLM trained in 2022 may lack knowledge about a new technology released in 2024. RAG solves this problem by retrieving real-time, external information before generating responses, making AI-generated text more accurate, relevant, and up to date.

How RAG Works

Building a Personalized Recruitment System with RAG
HR Tech
Hello, this is Woosik Na, CEO of TalentSeeker.
At TalentSeeker, we continuously refine our platform through monthly updates to deliver a superior hiring experience.
In this update, we have integrated headhunting expertise into AI-driven hiring solutions to revolutionize recruitment. Our focus is on work-based candidate evaluation, enhanced screening insights, and a unified dashboard, helping our users find the right talent faster and more accurately.
Let’s explore the key new features and how they improve the hiring process.

1. Work-Based Candidate Evaluation

Hiring the right talent ultimately comes down to how well a candidate can perform on the job. Our latest update moves beyond traditional resume-based screening by enabling evaluation based on actual job performance.
Identify candidate strengths through past projects and work history
Leverage role-specific assessments to validate real-world skills
[This Month's Update] Reinventing Headhunting with AI
Talent Sourcing
Recruitment CRM
Hello! This is the TalentSeeker Operations Team.
Our Weekly Case Study analyzes TalentSeeker user activity logs over the past week to identify hiring challenges and help you overcome them. By examining real job postings, we aim to provide practical insights that can streamline your hiring journey.
Do you have a hiring case you need help with? Feel free to reach out via our KakaoTalk channel to discuss your challenges. Your feedback plays a crucial role in improving our service!

Hiring a PHP-Based ERP System Developer

This week’s case study focuses on “Hiring a PHP-Based ERP System Developer.”
Since PHP is a less in-demand tech stack, finding experienced developers proficient in it can be challenging. Additionally, ERP and e-commerce platform development require a broad skill set, making the hiring process even more complex.
We’ll walk you through how to leverage TalentSeeker’s advanced filtering features to efficiently find the right candidate for your company. By refining search criteria based on location, skills, experience, education, and more, you can quickly and effectively identify top talent.

Selecting the Job Category Filter

[Case Study] Hiring an Online Shopping Mall ERP System Developer
Case Study
A job title on a business card doesn’t define real talent. What companies truly need to know is: "What results can this person deliver?"
Many companies still focus on hiring a "3-year experienced developer" or a "project manager", but this approach is no longer effective. In fact, 70% of companies report that the talent they hire fails to meet expectations.
The world is evolving rapidly, and the challenges businesses face are becoming increasingly complex. This means that hiring criteria must shift from title-based hiring to task- and project-based hiring. If companies want to find the right talent, they must focus on the actual work that needs to be done.
Here are three essential strategies to help businesses hire more effectively by focusing on work rather than just job titles.

1. Break Down Work Into Tasks and Projects

The first step is to stop relying solely on job descriptions and resumes. In today’s complex work environment, traditional job descriptions are no longer sufficient.

Why Are Traditional Job Descriptions Ineffective?

Most job descriptions are outdated or filled with vague expectations. For example, when hiring a developer, stating "teamwork skills" or "problem-solving abilities" doesn’t provide clarity on the actual work that needs to be done.
Job Titles vs. Real Work: Hiring Based on Impact, Not Labels
Sourcing Strategy
Traditional hiring methods have long focused on education, years of experience, and job titles. However, these criteria no longer guarantee business success. In today’s fast-changing business landscape, what companies truly need are candidates who can deliver real results. This is why Skill-Based Hiring and Task-Based Hiring are emerging as the future of recruitment.
While traditional hiring is still widely used, its limitations are becoming increasingly clear. According to Gartner's research, 45% of hiring failures are due to poor job performance. This is because superficial credentials don’t always reflect actual skills. Many HR professionals have experienced hiring candidates with impressive resumes, only to find that they lack the necessary practical skills. Given that hiring directly impacts productivity, more companies are now shifting their focus toward performance-based hiring.

What’s Wrong with Traditional Hiring?

Traditional hiring relies on resumes, degrees, job titles, and certifications to evaluate candidates. While this approach can be convenient, it often fails to assess a candidate’s actual ability to perform a job.
In the past, degrees from top universities and long years of experience were strong indicators of a qualified candidate. However, in today’s workplace, these factors are no longer reliable predictors of success. For example, hiring a “Frontend Developer with three years of experience” doesn’t guarantee that the candidate can effectively execute real-world projects or integrate smoothly into a team.
Traditional hiring also excludes skilled but non-traditional candidates. Many professionals have developed expertise through self-study, online courses, or hands-on experience but struggle to pass hiring filters that prioritize formal education. To overcome these issues, companies are adopting Skill-Based Hiring and Task-Based Hiring as more practical and effective alternatives.

Two Practical Hiring Approaches

Skill-Based Hiring: Focusing on Abilities, Not Resumes

Why Traditional Hiring No Longer Works
Sourcing Strategy
If you’re a recruiter, you’ve likely experienced the frustration of a lack of applicants, passive candidates ignoring your emails, and unanswered outreach attempts.
Passive candidates may not be actively job hunting, but they can still be interested in the right opportunity. However, expecting to engage them with just a single email is unrealistic. According to a report by Lever, a well-structured email sequence can increase response rates by up to 41%.
Recruiting is fast-paced, and candidate attention spans are limited. That’s why a strategic email sequence, rather than a one-off email, is essential for building meaningful connections. In this guide, we’ll explore how to reduce friction in outreach and successfully engage passive candidates with an optimized email sequence strategy.

Crafting Personalized Email Templates

Experienced recruiters know that a one-size-fits-all email never works. The content, tone, and timing of each email need to be tailored to different roles, industries, and candidate personas.

Key Elements of a Strong Email Template

•
Subject Line: Keep it short, clear, and include a personalized element (e.g., the candidate’s name or role) for higher open rates.
•
Customization Variables: Use placeholders for details like the candidate’s name, company, and location to make emails feel highly relevant.
•
Save Your Templates: Never lose a well-crafted email! Saving templates allows for easy reuse and iteration.
Unresponsive Candidates, Passive Talent, and No Replies? Here’s How to Fix It
communication strategy
The Christmas season isn’t just about wrapping up the year—it’s a prime opportunity for companies to enhance their employer brand and stand out in the talent market. Studies show that 60.9% of employees have come to recognize the importance of company culture in the post-pandemic era. Additionally, according to the MIT Sloan Management Review, "Respect" is one of the most critical factors in evaluating corporate culture. This highlights how creating a positive employer image is essential for successful hiring.
By integrating HR branding and recruitment campaigns with the holiday season, companies can cultivate a warm, engaging presence and leave a lasting impression on potential candidates. This article explores effective strategies to leverage the Christmas season for recruitment and employer branding.

Christmas Email Campaigns

Are your Christmas emails simply holiday greetings?
Instead, use your year-end messages as an opportunity to showcase achievements and reinforce your employer brand. For instance:
"This year, our company successfully launched new projects, achieving 20% growth. We couldn't have done it without you—thank you!"
A message like this fosters trust and appreciation. Here’s a sample Christmas recruitment email template you can use:
Standing Out in the Job Market with a Christmas HR Campaign
communication strategy
Hello! This is the TalentSeeker Operations Team
Our Weekly Case Study analyzes TalentSeeker user activity logs over the past week to identify and address common challenges in the hiring process. By reviewing real hiring cases, we aim to provide practical solutions to support your recruitment journey.
Do you have a hiring challenge you’d like help with? Feel free to reach out to us anytime via our KakaoTalk channel—your valuable input helps us improve our services and better support your hiring needs!

Hiring a Linux Application Developer

This week’s case study focuses on “Hiring a Linux Application Developer.”
Linux application developers require an in-depth understanding of the Linux system architecture and kernel, along with expertise in Linux NIC driver development, C and Python-based system and network programming, and network security technologies such as firewalls, IPS, and NAT.
Additionally, knowledge of network protocols like HTTP, SSL, and SMTP, experience integrating databases like MySQL and SQLite, and proficiency in interface and routing system design, proxy development, and cloud-based network security solutions are highly preferred.
Finding candidates with this specific skill combination is challenging, making the hiring process longer and more complex.
To streamline this process, TalentSeeker’s advanced filtering features can help you pinpoint the right candidates efficiently. By setting precise criteria such as job role, responsibilities, required skills, and experience level, you can quickly and effectively narrow down your candidate pool and find the perfect fit for your company.

How to Find the Right Talent with TalentSeeker’s Filters

[Weekly Case Study] Hiring a Linux Application Developer
Case Study

Introduction

Identifying top talent is just one part of successful hiring—engaging candidates in a compelling way is just as critical. As AI technology rapidly advances, this process has evolved beyond mere efficiency. Now, AI enables more sophisticated and personalized communication that not only captures attention but also builds trust with candidates.
In this article, we'll explore how to use ChatGPT to craft effective, personalized recruiting emails. We'll focus on prompt engineering techniques that maximize AI's capabilities and ensure you get the best results, with practical examples to guide you.

What is Prompt Engineering?

Prompt engineering is the process of designing inputs to optimize an AI model’s output. This technique ensures that AI properly understands the given task and produces high-quality responses.
Since natural language processing (NLP) models operate based on language patterns and statistical relationships, well-structured prompts help them generate more accurate and relevant responses. A well-crafted prompt provides context and clarity, significantly improving the model’s output.
Among OpenAI’s recommended six prompt engineering strategies, we’ll focus on the following three key techniques:

Three Essential Prompt Engineering Techniques

Crafting Personalized Recruiting Emails with Prompt Engineering (feat. ChatGPT)
HR Tech
As a recruiter, communication with candidates directly impacts key hiring KPIs. When reaching out to applicants, they are likely expecting your email, increasing the chances of a quick response. However, when sourcing passive candidates, getting them to open and respond to your email can be much more challenging.
Regardless of the situation, crafting a compelling subject line is crucial. Studies show that 33% of recipients decide whether to open an email based solely on the subject line. While there may be differences between applicants and sourced candidates, the fundamental principles for writing an effective subject line remain the same. Here are some proven tips to optimize your recruitment email subject lines.

Personalize Your Message

Candidates may be receiving emails from dozens—if not hundreds—of recruiters. No one wants to feel like they are just another recipient of a mass email.
At the very least, include the recipient's name in the subject line. Research shows that emails with personalized subject lines have a 50% higher open rate than generic ones.
Go beyond just their name—incorporate their field of expertise. For example:
“An Opportunity to Elevate Your Career as a Data Engineer!”
“Ready to Work on the Cutting Edge of AI Research & Development?”
Of course, writing 100 unique subject lines manually isn’t scalable. However, with AI-powered tools, personalization becomes significantly easier and more efficient.
How to Write Email Subject Lines to Increase Candidate Open Rates
outbound recruiting
The recruitment landscape is rapidly evolving, and effective communication with candidates has become a key factor in a company’s success. Traditional email communication methods, which often resulted in low response rates, have proven to be limited. Today, text messaging has become the new standard, offering more immediate and personalized communication.
Text messaging is more than just a contact tool—it plays a crucial role in creating a positive experience for candidates and building trust when approached strategically. However, indiscriminate texting can backfire, making it important to use this method thoughtfully.
This article explores why text messaging is a powerful tool in the recruitment process, how it helps build strong relationships with candidates, and how to use it professionally without compromising on expertise.

Why is Text Messaging Effective While Email Isn't?

Research shows that text messages boast an open rate of 98%, significantly outperforming emails with an average 20% open rate (SMS Comparison). This is because text messaging has become a personal communication channel. People check their text messages more frequently than emails, and the response times are faster. Therefore, strategically using text messaging in recruitment can greatly enhance communication efficiency.
However, text messages are much more immediate and personal than emails, so the content and timing of the messages must be handled carefully to avoid eroding trust.

Don’t Just Send Texts—Engage with Candidates

How to Attract Talent with Text Messaging
communication strategy
Hello! This is the TalentSeeker Operations Team
We’re excited to introduce our Weekly Case Study, a new initiative aimed at helping recruiters optimize their hiring processes.
Each week, we analyze TalentSeeker user activity logs to identify common hiring challenges and provide data-driven solutions. By examining real-world job postings, we offer practical insights to enhance your recruitment journey.
Do you have a hiring challenge you’d like us to explore? Feel free to reach out via our KakaoTalk channel, and let’s tackle it together! Your valuable input helps us improve our services and create better hiring experiences.

Hiring a Foreign Cloud-Native BSS Backend Developer

This week’s case study focuses on “Hiring a Foreign Cloud-Native BSS Backend Developer.”
Both cloud-native environments and Business Support Systems (BSS) require highly specialized skill sets and deep expertise. For instance, a strong understanding of Kubernetes, Docker, and microservices architecture is essential, along with experience in specific BSS protocols and system configurations.
Due to this niche combination of skills, finding the right talent can be challenging, often leading to prolonged hiring processes.
Using TalentSeeker’s advanced filtering tools, we’ll demonstrate how to efficiently identify and connect with the perfect candidate. By applying filters for job roles, required skills, experience, and education, you can streamline your hiring process and discover top talent faster.
[Case Study] Hiring a Foreign Cloud-Native BSS Backend Developer
Case Study
In recent years, companies have invested significant resources into talent acquisition strategies. However, just like even the best recipes can't create a perfect dish without fresh ingredients, even the most advanced talent acquisition strategies lose their effectiveness without a sufficient pool of candidates. Talent sourcing, which ensures a robust talent pool, has become a key element in determining the success of recruitment.

What is Talent Sourcing?

Talent sourcing is the proactive process of finding and connecting with potential candidates. It typically targets passive candidates—those who aren't actively seeking new jobs but may be interested if a better opportunity arises. Unlike traditional methods, which rely on posting job openings and waiting for applicants, talent sourcing actively seeks out individuals who may not be looking for work at the moment. For example, many top talents are already employed elsewhere and aren't actively job hunting, but they may show interest if presented with a better opportunity. Talent sourcing aims to uncover these passive candidates, engage with them, and build relationships so that when the right moment comes, the organization can secure the right talent.

Why Talent Sourcing Has Become More Important

Today’s businesses can no longer attract enough talent with just job postings. First, the rapid advancement of technology has led to a surge in demand for highly skilled professionals in areas like artificial intelligence, data analysis, and software development. According to a report from CodeSignal, 78% of tech companies in the U.S. are struggling to hire developers, with the process often taking over six months. This challenge is exacerbated by the fast-growing demand for talent in AI, data science, and cybersecurity, while the supply of skilled workers remains limited. Additionally, McKinsey's report predicts a shortage of around 85 million tech workers by 2030, deepening the imbalance between supply and demand for skilled developers.
The second factor is that modern candidates have more information and options than ever before. Job seekers today are more informed and have higher expectations for their career development. Platforms like LinkedIn, review sites like Glassdoor, and various recruitment apps allow candidates to easily access information about potential employers. According to a survey by Glassdoor, 79% of job seekers check a company's reviews and reputation before applying. Additionally, with the increasing demand for remote and flexible work options, companies that don’t offer these benefits may struggle to attract top talent. This shift reflects how candidates today expect better career growth opportunities, work-life balance, and a positive company culture.
As a result, simply posting job openings and waiting for applicants is no longer enough to attract high-quality candidates. Proactive talent sourcing has become essential. Companies need to engage with and form relationships with top candidates to attract their interest.
Talent Sourcing: No Longer an Option, But a Necessity
Talent Sourcing
Hello! This is the TalentSeeker Operations Team
We would like to introduce our new Weekly Case Study.
The Weekly Case Study involves analyzing the activity logs of TalentSeeker users over the course of a week to help address the challenges they face in the recruitment process. Based on the cases we analyze ourselves, we aim to provide practical assistance in your recruitment journey.
Do you have any recruitment cases you would like to discuss? Feel free to reach out to us anytime through our KakaoTalk channel. Your valuable feedback helps us create better services.

Hiring for Infrastructure Lead at a Social Media Company

This week’s case study topic is "Hiring for Infrastructure Lead at a Social Media Company".
We’ll introduce how to effectively find the right talent for your company by utilizing TalentSeeker's various filter functions. By setting detailed criteria such as location, skills, experience, and education, you can narrow down your search and quickly and efficiently find the talent you need.

Choosing Job Role Filters

[Case Study] Hiring an Infrastructure Lead for a Social Media Company
Case Study
In today's competitive job market, companies are moving away from relying solely on intuition and experience and are instead adopting data-driven approaches to secure top talent. Data-driven recruitment helps companies optimize and evaluate each stage of the hiring process, allowing them to find the right candidates more accurately and efficiently. So, how can this data-based approach be used to achieve successful hiring?
In the past, many companies relied heavily on resumes and interviews to make hiring decisions. However, this approach had its limitations, as it often failed to assess candidates' actual skills or cultural fit. Relying on fragmented information led to unexpected adaptation issues and underperformance. With the advancement of technology, however, companies can now collect and analyze data from various sources to make more reliable hiring decisions.

Data Collection and Analysis

The first step in a data-driven approach is collecting data about candidates. This involves gathering information from various sources, including resumes, job applications, and interview records. Additionally, non-traditional data sources such as candidates' social media activities or online portfolios can help provide a more accurate picture of their experiences and skills.
By analyzing the collected data, companies can evaluate not just the quantitative aspects such as education, work experience, and technical skills, but also qualitative factors like personality, work style, and cultural fit. This helps companies predict how well a candidate will align with the company's culture and how likely they are to succeed in the role.
To enable data-driven hiring, a large volume of candidate data is essential. Without systematically organized data, AI automation is ineffective, and without enough candidates, AI evaluations are meaningless.
TalentSeeker is an HR solution that effectively provides this data, focusing on talent sourcing and helping companies secure the right candidates in advance.
Harnessing Data-Driven Recruitment: How AI and Analytics are Revolutionizing Hiring
Recruiting Management
Sourcing Strategy
In recent years, the HR industry has undergone significant changes. The rise of digitalization and artificial intelligence, the expansion of remote and flexible work, and the fierce competition for talent have led HR professionals to adopt new strategies.
Moving away from traditional hiring methods, there is now a need for a systematic approach to continuously search for and retain talent. Today, the competitive edge in HR is determined by how effectively a company manages its talent pipeline.
The importance of managing the talent pipeline is increasing day by day, and more companies are beginning to realize this. However, executing this process in reality is not an easy task. According to a 2023 survey by HR Professionals Network, 67% of global companies acknowledge the importance of pipeline management, but only 30% have successfully implemented it. This gap highlights how talent pipeline management is an ideal goal, yet difficult to achieve in practice.

Talent Pipeline and Recruitment Competitiveness

The recruitment competitiveness of companies today stems from how well they manage their talent pipelines. Simply having a large number of LinkedIn followers does not mean you have a talent pipeline. A true talent pipeline is not just about the number of followers or expressions of interest, but rather about building relationships that are ready to be leveraged when the need arises.
Having many followers may indicate potential talent connections, but it does not guarantee they are ready for hiring. A talent pipeline is about maintaining ongoing relationships and understanding candidates’ needs, so that when the right time comes, they are prepared to match your company's needs. For instance, even if you have many followers, if you haven’t collected sufficient information about them or if there is no trust established with your company, it may not translate into actual hires. A true pipeline builds relationships with candidates, keeping them prepared to be hired when needed.

The Essence of Talent Pipelines

Talent Pipeline Management: Essential Strategies for Strengthening HR Trends and Boosting Recruitment Competitiveness
Talent Pipeline
Talent Sourcing
Recruiters are always putting in significant effort to find the right talent for their companies. Hiring is already a demanding and intensive task. Amid the new expectations and changes that have arisen since the pandemic, talent acquisition professionals are now requiring more skilled candidates. However, the supply of such highly skilled talent is currently lacking. There are certain roles in the market that are relatively harder to fill compared to others. In this article, we will explore the most difficult roles to hire for and how recruiters can quickly fill them.

Why is There a Talent Shortage?

70% of global HR leaders report that talent shortages are worsening. HR teams are struggling to include qualified candidates in their pipelines to meet hiring goals. According to Korea Employment Information Service, as of September 2024, the number of job openings is double that of job seekers, leaving many companies struggling with hiring. This trend is also seen in the United States, where there are nearly two job openings for every job seeker, and 61% of U.S. business leaders report that attracting top talent is difficult.
The talent shortage is especially prevalent in knowledge-intensive industries such as technology, healthcare, media, and telecommunications. According to McKinsey's report, 90% of companies are expected to face even greater talent shortages by 2030. This is due to the growing skills gap created by digitalization and automation, which limits the availability of skilled professionals.
One of the main causes of talent shortages is voluntary, unexpected turnover, i.e., “resignation.” Many workers are reevaluating their life priorities due to increased family care responsibilities, work fatigue, and deteriorating physical/mental health. These factors have led many workers to opt for resigning, ultimately contributing to the talent shortage in the labor market.

Most Difficult Jobs to Fill

The hardest roles to fill are in technology, medical expertise, sales, and product/project management. These positions face significant talent shortages, and here’s why recruiters are struggling to fill them.
Top 4 Most Challenging Roles to Hire For
Recruiting Management

Introduction

Finding the right candidate for a job is a time-consuming and effort-intensive process for recruiters. Even when evaluating candidates from an initial talent pool, there’s always the possibility of missing out on potential candidates. By leveraging AI technology, these issues can be addressed, and more efficient recruitment processes can be established.
AI-powered recommendation systems learn from recruiter feedback (e.g., Good Fit or Not Fit) and can re-recommend candidates who were initially overlooked but are potentially well-suited. In this post, we'll look at how recommendation systems work with text data and explore the core technologies behind them using a simple example.

TF-IDF

TF-IDF (Term Frequency-Inverse Document Frequency) is a technique used to assess the importance of words in a document. It gives higher weight to words that appear frequently within a document but rarely across all documents, indicating their significance to the content.

TF (Term Frequency)

This refers to the frequency of a word. It calculates how often a particular word appears in a document.
TF(t)=Count(t,D)Total words in DTF(t)={{Count(t,D)}\over{Total ~words~in~D}}

IDF (Inverse Document Frequency)

[HR Tech] Talent Sourcing with AI: Feedback-Based Candidate Recommendation System
HR Tech
Hello! This is the TalentSeeker operations team
We are excited to introduce our new Weekly Case Study!
The Weekly Case Study is a time where we analyze the activity logs of TalentSeeker users over the course of a week and work together to solve the challenges you face in the recruitment process. Based on the cases we analyze, we aim to provide practical help for your recruitment journey.
Do you have a recruitment case you'd like to solve? If you’d like to share your challenges with us, feel free to reach out via our KakaoTalk channel anytime. Your valuable feedback will help us create better services!

Hiring a Watermark Algorithm Developer for a Security Company

This week's case study is titled "Hiring a Watermark Algorithm Developer for a Security Company".
We’ll be introducing how to effectively find the right talent for your company using TalentSeeker’s various filtering features. By setting specific conditions like location, skills, experience, and education, you can narrow down the pool of candidates and find the right talent quickly and efficiently.
[Case Study] Hiring a Watermark Algorithm Developer for a Security Company
Case Study
As competition in the hiring market intensifies, strategic approaches for effectively acquiring talent are becoming increasingly important. In particular, the key metrics that can be measured at each stage of the recruitment process are essential tools for recruiters to make better decisions and improve the overall hiring experience. In this article, we introduce important terms and key metrics related to the recruitment process.

Talent Intelligence

Talent Intelligence refers to the strategy of collecting and analyzing talent-related data to optimize recruitment and talent management. It helps make better hiring decisions by analyzing market changes and talent trends.
The most important metric in Talent Intelligence is Time to Fill. This metric plays a crucial role in evaluating hiring speed and is the first step towards efficient talent acquisition. The second key metric is Time to Hire, which shows how quickly a decision is made about a particular candidate, emphasizing the importance of quick communication and decision-making with candidates. The third important metric is Time to Source, which helps measure the efficiency of sourcing strategies and highlights how quickly suitable candidates are found—an essential factor for recruitment success.
•
Time to Fill: Measures the time it takes to fill a specific position. This metric plays a key role in evaluating the speed of the hiring process and is crucial for developing strategies to improve efficiency.
•
Time to Hire: Refers to the time from when a candidate starts the hiring process until they are officially hired. This metric shows how quickly hiring decisions are made for specific candidates.
•
Time to Source: Refers to the time taken to find and engage suitable candidates after the job posting. This gap helps assess the health of the talent pipeline and the efficiency of the sourcing process.
If Time to Fill is too long, it may indicate difficulties in finding suitable candidates during the sourcing process. The greater the difference between Time to Hire and Time to Fill, the longer it took to search for candidates, signaling the need to improve the sourcing strategy. Many companies traditionally focus on Time to Hire and pay less attention to Time to Source. This can lead to neglecting the importance of quickly adding suitable candidates to the pipeline and hinder efforts to improve sourcing strategy efficiency.
Recruitment Terminology – Recruitment Metrics
Recruiting Management

Recruitment Process Management: A Key to Talent Acquisition

In recent years, the recruitment market has become more challenging than ever. Talent is scarce, demands are rising, and with the constant stream of layoffs and job postings, recruitment has become progressively harder. Recruitment expert Annie Griffin stated, "We not only need to find talent, but we also need to survive the competition. The current situation is truly a survival game."
Recruiters across various anonymous online communities have shared their daily struggles. Often, recruiters go unrecognized and face many challenges, many of which are beyond their control.
At TalentSeeker, we understand the challenges you’re facing. Today, we will explore five key challenges that recruiters commonly face during the recruitment process.

ATS (Applicant Tracking System)

An ATS is a software system used to manage the recruitment process. It tracks everything from application submission to interviews and final decisions, helping recruiters manage all candidate information centrally and evaluate it fairly.
Main features include:
•
Collecting and managing applications: Collects resumes and all related information from candidates and manages them centrally.
•
Managing recruitment stages: Tracks the progress of each stage in the hiring process and records necessary actions at each step.
Recruitment Terminology – The Hiring Process
Recruiting Management
In recent years, the hiring market has faced more challenges than ever. Talent is scarce, demands are increasing, and large layoffs alongside a constant influx of recruitment requests have made hiring progressively harder. Recruitment expert Annie Griffin remarked, “We not only have to find talent, but we must survive the competition. The current situation feels like a survival game.”
Recruiters across various anonymous online communities also express their daily struggles. Recruiters are often unrecognized and constantly face challenges, most of which are beyond their control.
At TalentSeeker, we understand the challenges you're facing. Today, let's look at the five main challenges recruiters commonly face.

Playing the Mediator Role with All the Responsibility

Recruiters are often caught in the middle, blamed by both candidates and hiring managers, and placed at the center of conflicts.
While recruiters can influence the candidate experience to some degree, they are not always in a position to control every situation. For instance, recruiters may try their best but are often unable to provide feedback to rejected candidates. In some cases, recruiters themselves may not even know why a candidate was rejected.
On the hiring manager’s side, recruiters often face blame for not providing the desired talent pool. This is especially true when hiring managers have high expectations for a position but can offer limited conditions to candidates. In these situations, recruitment feels like an unwinnable battle.

Unrealistic Expectations from Hiring Managers

5 Challenges Recruiters Face in the hiring process
Recruiting Management
Hello! This is the TalentSeeker Operations Team
We are excited to introduce our new Weekly Case Study!
The Weekly Case Study involves analyzing the activity logs of TalentSeeker users over the course of a week to collaboratively address the challenges they face in the hiring process. Based on the cases we analyze, we aim to provide practical support to enhance your hiring journey.
Do you have a hiring case you'd like help with? If you’d like to discuss it with us, feel free to reach out anytime via our KakaoTalk channel. Your valuable feedback helps us create even better services!

Hiring a Platform Backend Developer for a Pharmaceutical Company

This week's case study topic is "Hiring a Platform Backend Developer for a Pharmaceutical Company."
We’ll show you how to effectively find the perfect candidate for your company by utilizing TalentSeeker’s various filtering features. By setting detailed criteria such as location, skills, experience, and education, you can narrow down the search and quickly and efficiently find the right candidate.
[Case Study] Hiring a Platform Backend Developer for a Pharmaceutical Company
Case Study
In today’s competitive hiring market, talent sourcing is one of the most challenging tasks. Recruiters often face difficulties in finding top talent and building relationships with candidates.
However, approaching candidates using contact information that wasn’t directly provided can trigger negative emotions such as confusion, fear, or anger. This can lead to a poor recruitment experience, which could damage your company's image.
While open web platforms are full of candidate information, it's crucial to approach the process of gathering contact details ethically and responsibly. This article will guide recruiters on how to effectively acquire candidate contact information while maintaining transparency, and how to explain to candidates how their information was obtained.

Using Professional Networking Platforms

Platforms like LinkedIn and GitHub are essential resources for recruiters looking to connect with potential candidates. These platforms allow users to showcase their skills and experience, and often make contact details such as email addresses or social media profiles easily accessible. When reaching out to candidates on these platforms, it’s important to personalize your message and show genuine interest in their qualifications.

Exploring Personal Websites and Portfolios

Many top candidates have personal websites or portfolios where they showcase their work and achievements. These sites often include contact information or a dedicated Contact page. Recruiters can use targeted search engines or portfolio discovery tools to find valuable candidate contact information.
Candidate Contact Information Acquisition Strategy for Recruiters
outbound recruiting
Talent Sourcing

Introduction

As competition in the hiring market intensifies, it has become increasingly important for companies to find the right candidates quickly and efficiently. Traditional recruitment methods not only consume a lot of time and resources but are also prone to errors. The advancement of artificial intelligence (AI) offers new possibilities to address these challenges. Specifically, AI-based talent sourcing using natural language processing (NLP) models like SBERT (Sentence-BERT) is bringing innovative changes to the recruitment process.
SBERT can accurately analyze the similarity between resumes and job descriptions, allowing companies to recommend the most suitable candidates for the positions they need to fill. In this article, we’ll explore how SBERT can be used in recruitment processes through a simple example.

What is Natural Language Processing (NLP)?

Natural Language Processing (NLP) is a technology that allows computers to understand and process human language. NLP includes converting text or speech data into a format that machines can comprehend and analyzing the meaning of language to apply it in various applications. Examples of NLP applications include document classification, sentiment analysis, and machine translation. In talent sourcing, NLP plays a critical role by analyzing textual data such as resumes or job descriptions to identify suitable candidates.

What is Embedding?

Embedding is a technique used to convert natural language data into high-dimensional vectors. While human language is highly complex and varied, computers process numerical data much more effectively. To address this, text is transformed into high-dimensional vectors that preserve the meaning of the words. These vectors enable the calculation of semantic similarity between words, ensuring that words with similar meanings are positioned close to each other in the vector space.

Cosine Similarity

Cosine similarity is a method for measuring the similarity between two vectors by determining how closely their directions align. The magnitude of the vectors is ignored, and only the angle between the vectors is considered. The closer the angle is to 0 degrees, the higher the similarity, and the closer it is to 90 degrees, the lower the similarity.
Cosine similarity is typically calculated using the following formula:
cosine similarity=A⋅B∥A∥∥B∥\text{cosine similarity} ={{A⋅B}\over{∥A∥∥B∥}}
[HR Tech] AI-Based Talent Sourcing: Finding the Right Candidates with Sentence-BERT
Talent Sourcing
HR Tech
Hello! This is the TalentSeeker Operations Team
We are excited to introduce our new Weekly Case Study!
The Weekly Case Study involves analyzing activity logs from TalentSeeker users over the course of a week, focusing on the challenges you face in the hiring process. Based on the cases we analyze, we aim to provide practical support to enhance your hiring journey.
Do you have a hiring case you would like help with? If you'd like to discuss it with us, feel free to reach out via our KakaoTalk channel anytime. Your valuable feedback helps us create even better services!

Hiring a Data Analyst for a Global Consulting Firm

This week's case study is focused on "Hiring a Data Analyst for a Global Consulting Firm."
We’ll introduce how to effectively find the right candidate for your company by utilizing TalentSeeker's various filter functions. By setting specific criteria like location, skills, experience, and education, you can narrow down the search and quickly and efficiently identify the perfect fit for your needs.
[Case Study] Hiring a Data Analyst for a Global Consulting Firm
Case Study
Artificial Intelligence (AI) is no longer just an emerging trend—it has become a core part of everyday life and business, making it one of the most critical fields today. This is especially true for talent professionals. Whether you're a recruiter looking to hire AI specialists, a learning expert helping teams develop AI skills, or simply using AI tools to boost workplace productivity, AI is now an essential part of the equation.
You've probably heard that as AI advances, soft skills are becoming more valuable than ever. If technology can automate the technical (hard) skills of a job, then the human element (soft skills) becomes even more critical. This is why AI talent and strong soft skills have never been more important.
In this article, we’ll break down the top 10 soft skills that are essential when hiring AI talent. We’ll explore why they matter and how they enhance AI’s effectiveness in the workplace.

The Rapid Growth of AI Talent and AI Literacy

When we talk about AI talent here, we’re primarily referring to technical professionals with expertise in machine learning, natural language processing, and neural networks.
According to LinkedIn’s 2024 data report, the number of AI professionals has grown by over 20% in just one year. More companies are hiring for AI roles, and more professionals are building AI-focused careers. (Source: LinkedIn Global Data Report 2020-2023)
At the same time, AI literacy is skyrocketing among non-technical professionals. The number of people in non-technical roles taking AI courses has more than doubled in just six months. Project managers, graphic designers, and marketing professionals are all learning how to integrate AI tools into their workflows to increase efficiency and productivity.
With more professionals mastering tools like ChatGPT, Copilot, and Gemini, the definition of "AI talent" is expanding. AI literacy is no longer just for engineers and developers—it’s becoming an essential skill across industries.

The Soft Skills That Define AI Talent

The following 10 soft skills are particularly valuable for developing and using AI technologies effectively. When hiring AI professionals, it’s important to look beyond just technical qualifications and assess these key soft skills to identify well-rounded AI experts.
Top 10 Essential Soft Skills for AI Talent
Soft Skills
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