Microsoft Senior Applied Scientist L63 Interview Experience

Background: Senior Data Scientist with ~3 YOE ## Interview Process 1. Phone Screen (60 min) Format: Coding + Problem Solving Problem Solving: Behavioral scenarios and use cases

Coding Min Stack + fol

Background: Senior Data Scientist with ~3 YOE ## Interview Process 1. Phone Screen (60 min) Format: Coding + Problem Solving Problem Solving: Behavioral scenarios and use cases

Coding Min Stack +

follow-ups

Outcome

Passed to onsite 1. Onsite Loop (4 rounds, 60 min each)

Note: Recruiter's prep material was different from actual rounds for two rounds. 1.

Round 1 ML Fundamentals + ML Coding Actual Format: As described ML

Coding Implement K-means from scratch

Follow-up How would you vectorize this implementation? (I struggled a bit with matrix broadcasting) 1.

Round 2 ML Problem Solving + ML System Design Actual Format: ML fundamentals + coding (no system design) ML Questions (that I remember): - Reinforcement learning: Thompson sampling vs epsilon-greedy, explore vs exploit tradeoffs - Calibration: Platt scaling - Imbalanced data: Downsampling majority class

Coding Find max number of points on a line (2D array of points) I spent time handling floating point precision loss but got optimized solution 1.

Round 3 Data Analysis + Applied Sciences Actual Format: ML questions + coding ML Questions: - Offline metrics higher than online - why and how to address? - Data drift: Covariate shift vs label drift - Statistical tests for drift detection - Cold start problem for new ads - Explore/exploit tradeoffs - BERT vs GPT architecture and differences - Off-policy learning: "You have logged data from a model trained on an old policy, how would you fit a new model to update the policy?" (Found this confusing)

Coding Implement self-attention and masked self-attention I got mask syntax slightly wrong but overall code was correct and optimal otherwise. 1.

Round 4 Problem Solving + Coding (HackerRank) Format: As described

Coding Merge intervals ML Fundamentals: Bias-variance tradeoff, bagging, boosting, calibration, drift

Behavioral Standard behavioral questions (don't remember specifics) ## Key Takeaways - Prepare for coding in every round - ML fundamentals are crucial - specific topics depend on the team and role, but prepare for those thoroughly - Coding spans theory to implementation - be ready for everything from LeetCode to implementing ML algorithms from scratch ## Outcome Offer