Microsoft Data Scientist interview experience
Summary
I experienced a Microsoft Data Scientist interview which involved four technical rounds, focusing on data science fundamentals, feature engineering, metrics, and system design, with several specific questions in each area.
Full Experience
Data science fundamentals round (1 out of 4 tech rounds)
1. Data Quality & Outliers
Question: In a given dataset, some feature values are extremely large. How do you handle them? Do you remove, retain, or transform them?
Follow-up: What are other critical data quality issues you have faced in production systems?
2. Feature Engineering
Scenario: You are working for a subscription service experiencing high customer attrition (churn).
Question: What are the top 5 features you would engineer to predict user churn?
3. Metrics & Loss Functions
Question: How do you handle tasks that require strict attention to False Negatives (e.g., fraud or disease detection)? What specific performance metric do you optimize for?
4. System Design (Time-Series)
Scenario: You are receiving a streaming time-series data feed and need to detect anomalies. The constraints are extreme: it is highly latency-sensitive, and data arrives at 10,000 samples per second.
Question: What is the optimal architectural design for this? How do you balance the trade-off between algorithmic accuracy and system latency?
Interview Questions (4)
Data Quality & Outliers Handling
In a given dataset, some feature values are extremely large. How do you handle them? Do you remove, retain, or transform them?
Follow-up: What are other critical data quality issues you have faced in production systems?
Feature Engineering for Customer Churn Prediction
Scenario: You are working for a subscription service experiencing high customer attrition (churn).
Question: What are the top 5 features you would engineer to predict user churn?
Metrics & Loss Functions for False Negative Sensitive Tasks
How do you handle tasks that require strict attention to False Negatives (e.g., fraud or disease detection)? What specific performance metric do you optimize for?
System Design for Real-time Anomaly Detection
Scenario: You are receiving a streaming time-series data feed and need to detect anomalies. The constraints are extreme: it is highly latency-sensitive, and data arrives at 10,000 samples per second.
Question: What is the optimal architectural design for this? How do you balance the trade-off between algorithmic accuracy and system latency?