Microsoft Data Scientist Interview Loop
Microsoft's data scientist loop is a recruiter call, sometimes an online assessment, and a technical screen. Then three to five interviews in one block, usually virtual and back to back. Each interviewer writes feedback independently. The last round is with the hiring manager or a senior leader. Two kinds of role share this title. Product and analytics roles lean on SQL, experiments and business cases. Applied science roles lean on ML fundamentals, coding and system design. Read the job posting to know which loop you are in.
What decides it
Explaining why. Microsoft interviewers ask which metric you used on a past project and why, and keep asking. A/B testing is where most product candidates lose points, because they know the method and not the failure modes.
Practice by round
SQL basics done fast, on product telemetry: daily active users in a month, click-through rate by app, multi-app users. The assessment, when there is one, is a short coding test, so an hour of easy Python before the screen pays off.
SQL and Python at onsite level, like monthly active users and month-over-month change, or monthly returning users. Rolling retention and week-over-week comparisons are the shape Microsoft likes.
The product cases: a search ranking test where users share accounts, measuring a collaboration feature, and how to measure usage of a calling product. The business round in the real loop is one long case. Practice carrying a single scenario from metric definition through test design to diagnosing a drop. Then the mini-analysis, which is the same reasoning with real data.
| Case study | Difficulty |
|---|---|
| Skype Usage | Easy |
| Twitter Feed | Medium |
| Measuring a Collaboration Feature | Medium |
| Shared-User Search Ranking Test | Hard |
| Data project | Difficulty |
|---|---|
| Feature Adoption by Tenant | Medium |
Statistics fundamentals first: spotting an outlier, reading a box plot against a histogram, variance, skewed distributions and panel versus cross-sectional data. The real loop goes further into experiment design, Type I and II errors and evaluation metrics, and applied science loops add bias and variance.
| Question | Difficulty |
|---|---|
| New Observation is Outlier | Easy |
| Box Plot and Histogram | Easy |
| Definition of Variance | Easy |
| Panel vs Cross-sectional Data | Medium |
| Right vs Left-Skewed Distributions | Medium |