Kakao Hires for Applied Analysis and Ad Recommendation Teams via Winter Internship

Kakao is recruiting talent for its Applied Analysis Team and Ad Recommendation Team through a hiring-linked winter internship. While both teams prioritize data-driven decision-making and technological advancement, they differ clearly in scope and key performance indicators.

Applied Analysis Team: Creating Data Value

The Applied Analysis Team is responsible for discovering and maximizing value from Kakao's data assets. Key responsibilities include building profiling for materials and users, collaborating on service analysis and modeling, researching value discovery methodologies, and establishing the engineering foundation to support analysis.

Team members balance external requests and internal projects in an approximately 50:50 ratio, focusing on solving problems from a data perspective. The team operates with remote work as the default, supports task assignment tailored to members' tech stacks, and encourages cross-domain transitions.

Ad Recommendation Team: Real-Time ML Optimization

The Ad Recommendation Team is central to Kakao's advertising business, developing machine learning-based optimization logic models and serving real-time recommendation services. The goal is to calculate bid prices in real-time within the ad auction system and predict the value of expected actions such as clicks or conversions to achieve optimal performance.

To process large-scale real-time data, the team builds an MLOps environment and operates a serving system integrated with an A/B testing platform. It is an organization where system engineering and machine learning are tightly coupled, with efficiency improvements directly translating into business results.

Recruitment and Competency Assessment

This internship particularly focuses on hiring machine learning engineers for the Ad Recommendation Team. Rather than prioritizing bachelor's or master's degrees, the team verifies actual competencies such as data handling, analysis, and modeling project experience. Portfolios or Kaggle competition experience are considered a plus, and the ability to define problems and interpret data is evaluated heavily.