Microsoft Data Scientist candidates report project-focused screens and three-round processes that can combine Python, SQL, experimentation, machine learning, product cases, and behavioral discussion.
$198K
Avg. Base Comp
$275K
Avg. Total Comp
3 rounds
Typical Rounds
2 weeks
Process Length
Microsoft Data Scientist interviews reported here place substantial weight on explaining applied work, not simply naming tools or models. Prepare a defensible project narrative: candidates describe being asked to walk through prior projects, business impact, technical decisions, what they would change, and how an approach would behave at greater scale. One early screen used Python debugging rather than blank-page coding, while other reports describe Python problem-solving or live coding.
Technical coverage varies by team. Candidates report SQL using joins, aggregations, window functions, and edge cases, alongside product analytics and machine-learning discussion. Specific examples included diagnosing a change in a key business metric, identifying possible root causes and additional metrics, Python work with pandas and NumPy, ML and deep-learning concepts, fixing existing Python code, and implementing a 2D convolution operation. Practice communicating assumptions, tradeoffs, and next steps as you work through an ambiguous problem.
Behavioral and manager conversations also recur, often embedded among technical discussions. Candidates report questions about collaboration, stakeholder influence, competing priorities, setbacks, asking for help, feedback, and the impact of their work. Build concise examples that connect your contribution to a concrete decision or outcome. Evidence is thin on a single universal sequence, but several reports explicitly describe three rounds; one candidate reported a 30-minute screen followed by three 45-minute conversations.
Synthesized from 10 candidate reports by our editorial team.
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Real interview reports from people who went through the Microsoft process.
The final round was three 45-minute interviews with short breaks, and it leaned much more heavily on how I think about applied data science than on pure coding. After an initial screen, I met with interviewers who asked about my prior experience and research projects, including what I had worked on, how I evaluated machine learning models, and how I approached experimentation in a product setting. One question that stood out was describing a time I had to modify an existing algorithm and explaining the reasoning and impact behind the change.
The conversations also covered failure analysis, data-quality issues, and how I would connect a technical solution to practical product impact. There were behavioral questions throughout, such as discussing my research and examples from my past work, rather than treating behavioral as a separate, lightweight round. I found the difficulty to be more about clearly communicating sound judgment across ambiguous, real-world scenarios than solving a narrowly defined algorithmic problem under pressure.
I received an offer. I would prepare several detailed project and research stories, especially one where you changed an existing algorithm, one involving model evaluation or experimentation, and one where you diagnosed a failure or data-quality problem. Be ready to explain not only the technical work but also why it mattered in a product context.
Prep tip from this candidate
Prepare concrete stories about modifying an existing algorithm, evaluating ML models or experiments, and diagnosing failures or data-quality issues. Tie each technical decision to product impact, since the final interviews emphasized applied judgment and communication.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Microsoft
Given two sorted lists, write a function to merge them into one sorted list.
| Question | |
|---|---|
| First to Six | |
| Download Facts | |
| Employee Salaries (ETL Error) | |
| Random SQL Sample | |
| Raining in Seattle | |
| Find the Missing Number | |
| Minimum Change | |
| Scrambled Tickets | |
| Employee Project Budgets | |
| Find the First Non-Repeating Character in a String | |
| Bagging vs Boosting | |
| Find Bigrams | |
| Lowest Paid | |
| Same Side Probability | |
| P-value to a Layman | |
| Good Grades and Favorite Colors | |
| The Brackets Problem | |
| Project Budget Error | |
| Google Maps Improvement | |
| N-gram Dictionary | |
| Greatest Common Denominator | |
| Sequentially Fill in Integers | |
| Cyclic Detection | |
| Same Algorithm Different Success | |
| Longest Increasing Subsequence | |
| Precision and Recall | |
| Binary Tree Conversion | |
| Find Duplicate Numbers in a List | |
| Keyword Bidding |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an early conversation about their background, role interest, and resume experience. One report confirms a one-hour first conversation with a data scientist focused on CV experience; another screen included a project discussion and Python code debugging. Review the decisions and impact behind each relevant project.
Technical coverage varies: candidates report Python problem-solving or debugging, SQL, machine-learning concepts, analytics, and project follow-ups. Reported prompts included explaining log loss, evaluating models, modifying an algorithm, and discussing data-quality or failure analysis. Expect interviewers to probe the reasoning behind an answer.
Some candidates report an A/B-test design or product case. Examples included investigating a metric change, designing an experiment for a ranking change, considering interference, and recommending next steps. Candidates may need to clarify the problem, define metrics, surface root causes, and explain tradeoffs aloud.
Candidates report behavioral questions either as a distinct managerial round or throughout the loop. Topics included teamwork, stakeholder influence, competing priorities, setbacks, feedback, and work they were proud of. Use specific examples that show your role, judgment, and the business impact of the work.
