New York Life Insurance Company Data Analyst interview typically runs 4 rounds: online assessment, HR screening, behavioral interview, technical interview. The process took about a few weeks and was fairly straightforward, with a strong focus on fit and data quality.
$95K
Avg. Base Comp
$138K
Avg. Total Comp
4
Typical Rounds
2-4 weeks
Process Length
Our candidates report that New York Life is less interested in flashy analytics stories and more interested in whether you can be trusted with messy, operational data. A recurring theme is data quality: one candidate was asked directly how they ensure it, and the conversation kept circling back to data hygiene, internal tools, and how a new hire would fit into an existing workflow. That tells us the bar here is not just technical correctness, but whether you understand the discipline behind maintaining reliable data in a regulated environment.
We’ve also seen that the technical conversation goes beyond surface-level project summaries. The candidate noted that interviewers dug into past work to check whether they truly understood the concepts behind their own projects, which is a strong signal that polished resume bullets won’t carry much weight on their own. The SQL prompt about finding five random numbers reinforces the same pattern: they seem to prefer practical, low-drama problem solving over trick questions. In other words, they’re looking for someone who can explain their reasoning clearly, connect it to real work, and show they can operate inside a defined process without hand-holding.
Synthetized from 1 candidates reports by our editorial team.
Had an interview recently?
Share your experience. Unlock the full guide.
Real interview reports from people who went through the New York Life Insurance Company process.
Share your own interview experience to unlock all reports, or subscribe for full access.
Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at New York Life Insurance Company
Write a query to randomly sample a row from a big table.
| Question | |
|---|---|
| Bagging vs Boosting | |
| P-value to a Layman | |
| Hurdles In Data Projects | |
| Addressing Data Quality Issues | |
| Drink Production Allocation | |
| Client Solution Pushback | |
| Your Strengths and Weaknesses | |
| 2nd Highest Salary | |
| Employee Salaries | |
| Booking Regression | |
| Size of Joins | |
| Random Forest Explanation | |
| Lasso vs Ridge | |
| Scalped Ticket | |
| Missing Housing Data | |
| Assumptions of Linear Regression | |
| Three Zebras | |
| Classification and Regression | |
| Success Measurement | |
| Target Indices | |
| Data Preparation for Imbalanced Data | |
| Duplicate Rows | |
| Netflix Churn Prediction | |
| Type I and II Errors | |
| Modifying a Billion Rows | |
| Second Ace | |
| Overfit Avoidance | |
| Swap Variables | |
| Multicollinearity in Regression |
Synthesized from candidate reports. Individual experiences may vary.
The process starts with a basic online assessment that includes very simple coding problems. According to the interview experience, this was the easiest part of the process and focused on practical problem-solving rather than advanced technical difficulty.
Next is an HR screening that is mostly behavioral and centered on the candidate’s resume. Expect standard questions about past challenges, work experience, and general fit for the role.
The final stage includes a behavioral interview that stays fairly standard. Interviewers ask about your background, how you handle challenges, and how you would fit into the team and company culture.
The technical interview goes deeper into past projects and experiences to verify your understanding of the concepts behind your work. Expect questions about data quality, data hygiene, internal workflow fit, and practical SQL thinking such as finding 5 random numbers.