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The Interview Process
Coding & Math Screen
Testing Python (pandas/NumPy), SQL, and the mathematical foundations of machine learning (probability, linear algebra, calculus).
Technical Deep Dive
You will be grilled on a specific domain (e.g., forecasting ETA for Kakao T, or building recommendation systems for Kakao Webtoon/Melon). You must know the trade-offs of different model architectures.
Business Impact Panel
Assessing your ability to translate a 1% increase in an F1 score into actual business value (e.g., increased ride completion rates).
Real Kakao Interview Questions
Practice these exact questions faced by previous Data Scientist / Machine Learning Engineer (Kakao Brain / Mobility) candidates.
1For Kakao T (Taxi), predicting the exact ETA is critical. Walk me through the feature engineering and model architecture you would use (e.g., XGBoost vs. Graph Neural Networks) to accurately predict ETAs during highly volatile weather conditions in Seoul traffic. (Applied ML / Predictive Modeling)
2You are tasked with building a recommendation engine for Kakao Page (web novels). How do you balance exploiting the user's known reading preferences with exploring completely new genres to prevent them from getting stuck in a 'filter bubble'? (Recommendation Systems / Product Sense)
3Write a SQL query to calculate the '7-day rolling retention rate' for users who signed up via a specific promotional campaign on KakaoTalk, handling edge cases where users delete and reinstall the app. (Data Engineering / SQL)
4(Value: Impact) A complex deep learning model you built is 2% more accurate than the baseline Logistic Regression model, but takes 10x longer to execute inference, threatening the API latency SLA. Which model do you deploy to production, and how do you justify it? (Business Value / Execution)
5Tell me about a time you had an intuition about user behavior that contradicted the existing data 'dashboards'. How did you design an experiment (e.g., A/B test) to prove your hypothesis? (Data-Driven / Initiative)
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