Microsoft Senior Applied Scientist (L63) Interview Experience
- Experience
- 3 years
- Posted
- 6 Apr 2026
Summary
I completed a multi‑round interview process for a Senior Applied Scientist role at Microsoft, passing the phone screen and four onsite rounds covering ML fundamentals, coding, and problem solving, and ultimately received an offer.
How it went
Background: Senior Data Scientist with ~3 YOE
Interview Process
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Phone Screen (60 min) Format: Coding + Problem Solving Problem Solving: Behavioral scenarios and use cases Coding: Min Stack + follow-ups Outcome: Passed to onsite
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Onsite Loop (4 rounds, 60 min each) Note: Recruiter's prep material was different from actual rounds for two rounds.
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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)
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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
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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.
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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)
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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
Questions asked (13)
- 01
Implement K-means from scratch
Data Structures & Algorithms
Implement the K‑means clustering algorithm from scratch. After the implementation, discuss how you would vectorize the algorithm to improve performance, especially handling matrix broadcasting.
- 02
Find maximum number of collinear points
Data Structures & Algorithms
Given a 2‑D array of points, find the maximum number of points that lie on a single straight line. The solution should handle floating‑point precision issues efficiently.
- 03
Thompson sampling vs. epsilon‑greedy
Other
Explain the differences between Thompson sampling and epsilon‑greedy in reinforcement learning, and discuss the explore vs. exploit trade‑off.
- 04
Calibration with Platt scaling
Other
Describe how Platt scaling works for calibrating the probabilities of a binary classifier.
- 05
Handling imbalanced data via downsampling
Other
How would you handle an imbalanced dataset by downsampling the majority class?
- 06
Offline metrics higher than online metrics
Other
Why might offline evaluation metrics be higher than online (A/B test) metrics, and how would you address this discrepancy?
- 07
Data drift: covariate shift vs. label drift
Other
Differentiate between covariate shift and label drift as forms of data drift, and explain how each can be detected.
- 08
Statistical tests for drift detection
Other
What statistical tests can be used to detect data drift in a production ML system?
- 09
Cold start problem for new ads
Other
Explain the cold‑start problem when introducing new advertisements into a recommendation system and possible mitigation strategies.
- 10
BERT vs. GPT architectures
Other
Compare the architectures of BERT and GPT models, highlighting key differences in design and typical use‑cases.
- 11
Off‑policy learning from logged data
Other
Given logged data generated by an old policy, how would you train a new model to update the policy (off‑policy learning)?
- 12
Implement self‑attention and masked self‑attention
Data Structures & Algorithms
Implement the self‑attention mechanism and its masked variant as used in transformer models.
- 13
Merge intervals
Data Structures & Algorithms
Given a collection of intervals, merge all overlapping intervals and return the resulting list of non‑overlapping intervals.