Milliman Data Analyst interviews, as three candidates describe them, center on SQL, walking through your projects, and explaining your analysis clearly. Two reports mention weak follow-up after interviews.
$88K
Avg. Base Comp
$110K
Avg. Total Comp
3-5 rounds
Typical Rounds
Not reported
Process Length
Across three candidate reports, the Milliman Data Analyst interview comes down to SQL fundamentals and how clearly you explain your thinking. All three candidates ended with no offer, so this is a picture of what was tested rather than what wins offers.
Two candidates describe SQL-heavy technical rounds. One was asked to read queries and explain what they did, which rewarded careful attention to detail over cleverness. Another says joins came up repeatedly, alongside basic data structures and algorithms, object-oriented concepts and detailed questions about resume projects. Interviewers in that report wanted the reasoning and trade-offs behind your decisions, not just the result.
The practical side matters too. One candidate was given a healthcare-related dataset and asked to report on it in whatever format made sense, so practice summarizing unfamiliar data quickly. Another was asked how they validate data, and advises bringing specific checks you have actually run rather than general principles.
Behavioral questions appeared inside technical rounds as well as in later conversations with senior staff or HR. Communication also affected the experience after the interviews. Two candidates report slow or absent updates, and one never received feedback. Asking about next steps at the end of each stage is sensible, and a polite follow-up may help. The reports cover a phone or recruiter screen, technical conversations and a final round, but the exact format may vary by team.
Synthesized from 3 candidate reports by our editorial team.
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Real interview reports from people who went through the Milliman process.
On paper this one moved fast. I came in through a referral, and the whole process was three stages: a recruiter screen in the first week, a hiring manager conversation in the second, and a technical interview in the third. The early conversations felt warm. The team seemed to genuinely enjoy talking with me, and the recruiter stayed in regular contact, so the pace felt like it was heading somewhere good.
The technical interview is where things changed. I was asked how I validate data. It's a fair question for an analyst role, and I'd recommend walking in with specific checks you have actually run rather than general principles. After that interview, the recruiter's messages stopped. I had to reach out several times just to get an update, and the silence lasted long enough that I stopped expecting a decision.
The outcome was no offer. I asked for feedback on where I fell short and didn't receive any, so I left without a clear sense of what to improve for next time.
If you're interviewing here, treat the technical round as the point where you need to be at your sharpest. Don't read a quiet stretch as progress. Ask the recruiter for a timeline at the end of each stage and follow up on a schedule instead of waiting to hear back.
Prep tip from this candidate
Be ready to explain concretely how you validate data, with specific checks you have actually run. After each stage, ask the recruiter for next-step timing, since updates here dropped off after the technical interview.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
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| Question | |
|---|---|
| Employee Salaries | |
| Random SQL Sample | |
| Size of Joins | |
| Bagging vs Boosting | |
| Assumptions of Linear Regression | |
| Booking Regression | |
| Duplicate Rows | |
| Hurdles In Data Projects | |
| P-value to a Layman | |
| Type-ahead Search | |
| Type I and II Errors | |
| HHT or HTT | |
| Justify a Neural Network | |
| Lasso vs Ridge | |
| Unified Inbox | |
| Random Forest Explanation | |
| Three Zebras | |
| Decision Tree Evaluation | |
| Scalped Ticket | |
| WAU vs Open Rates | |
| Swap Variables | |
| Target Indices | |
| Production Model Monitoring | |
| Data Preparation for Imbalanced Data | |
| RAG Strict Source Control | |
| Success Measurement | |
| Car Recommendation Architecture | |
| Classification and Regression | |
| Client Solution Pushback |
Synthesized from candidate reports. Individual experiences may vary.
Candidates describe an opening screen before any technical work. One reports a fairly standard phone screening, and another a resume screen. A third came through a referral and had a recruiter screen. Expect questions about your background and the projects on your resume. The exact format may vary.
One candidate reports an online assessment after the resume screen, meant to gauge foundational technical skills. Other reports do not mention one, so it may not apply to every team or hiring cycle. If you get one, brush up on SQL and basic programming concepts.
Technical rounds are where candidates describe the most substance. Reports mention reading SQL queries and explaining them, repeated join questions, basic data structures and algorithms, object-oriented concepts, and a question on how you validate data. One candidate also analyzed a healthcare dataset and presented findings in a format of their choosing. One candidate had back-to-back interviews with two team members.
Candidates describe a final conversation that mixes technical and behavioral topics. One met senior staff, another had an HR round on communication, cultural alignment and fit, and a third had a hiring manager conversation earlier in the process. Prepare to explain your decisions and trade-offs clearly, since one candidate says communication style mattered.
Two candidates report that updates dried up after later interviews, and one received no requested feedback. Candidates may want to ask for a timeline at the end of each stage and follow up on a schedule. All three reports ended in no offer, so treat this as a description of what happened to them rather than a typical timeline.
