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 + follow-ups
   Outcome: Passed to onsite

2. 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)

   2. 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

   3. 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.

   4. 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