Candidates describe Morgan Stanley Data Analyst interviews as practical and resume-driven, with SQL, Python and Excel exercises plus some finance terminology. The number and format of rounds varies by team.
$108K
Avg. Base Comp
$155K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Morgan Stanley Data Analyst interviews, as candidates describe them, lean practical. SQL, Excel and a close read of your resume matter more than abstract analytics theory. The mix of rounds changes from team to team, so treat any single process as one example rather than a fixed template.
The most repeated theme is SQL and hands-on data work. Data Analyst candidates report questions on left versus inner joins, other kinds of SQL joins, and how to remove duplicates. One candidate was asked to write functions that pull specific data from a SQL table. Another was asked about query optimization, and a third was asked to define ACID properties. Python also appears in more than one account, from pandas and CSV basics to multiprocessing versus multithreading. Be ready to explain these fundamentals clearly without overcomplicating them.
Excel is the second thread. One candidate who received an offer analyzed a worksheet live with surveillance-team managers and talked through their reasoning. The questions were specific, and the managers seemed to want real sector experience rather than polished but vague answers. Practice explaining what a dataset shows in a clear, structured way, not just how you built the analysis.
Interviewers also dig into your background. One candidate had two interviewers work through the resume line by line, with questions on Excel and financial modeling experience and why they wanted technology at Morgan Stanley. A VP prescreen reportedly put financial terms at about half the job, with questions on equities and equity derivatives. The same VP asked scenario questions: what to do when teams use different metrics, how to deliver a metric requested for tomorrow, and which BI tools the candidate had used. There was also a classic balance-scale puzzle. Coding difficulty varies: one final round included a live medium LeetCode-style problem, while other processes stayed with SQL and Python fundamentals.
Synthesized from 25 candidate 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 Morgan Stanley 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 Morgan Stanley
Find the missing integer from a array of consequtive integers
| Question | |
|---|---|
| Maximum Profit | |
| Size of Joins | |
| Get Top N Frequent Words | |
| Sort Strings | |
| Hurdles In Data Projects | |
| Success Measurement | |
| Assumptions of Linear Regression | |
| RAG Strict Source Control | |
| Implementing the Fibonacci Sequence in Three Different Methods | |
| Shortest Path Algorithms | |
| Ranking Metrics | |
| Why Do You Want to Work With Us | |
| Justify a Neural Network | |
| Your Strengths and Weaknesses | |
| Feedback Sentiment Analysis | |
| Parking Application System Design | |
| 2nd Highest Salary | |
| Empty Neighborhoods | |
| Rolling Bank Transactions | |
| Employee Salaries | |
| Comments Histogram | |
| Closest SAT Scores | |
| Top Three Salaries | |
| Monthly Customer Report | |
| Slacking Employees Salaries | |
| Experiment Validity | |
| 500 Cards | |
| Compute Deviation | |
| Bagging vs Boosting |
Synthesized from candidate reports. Individual experiences may vary.
One Data Analyst candidate describes a HireVue stage with standard behavioral prompts, including a question about a setback. Not every Data Analyst report includes this step, so it likely depends on the team.
One Data Analyst candidate describes an HR phone screen on background, motivation and why Morgan Stanley. Another report describes a 30-minute VP prescreen that went through the resume. It then covered SQL joins and deduplication, BI tools, delivering a metric on short notice, and basic equities and derivatives terms. Expect more than pure behavioral talk.
One candidate had a 30-minute interview with surveillance-team managers. They analyzed an Excel worksheet live, explained their thought process, and answered specific questions about what they saw. The managers seemed to want real sector experience rather than polished but vague answers.
One candidate had two back-to-back 45-minute in-person technical rounds with managers. The rounds covered SQL joins, data-retrieval functions, pandas, multiprocessing versus multithreading, deadlocks and query optimization. Another had a technical phone screen with a software engineer that leaned toward data structures, Python and SQL basics such as ACID.
One candidate's final round was with two hiring managers. It included live coding of a medium LeetCode-style problem, more technical questions on their background, and basic Java and C++ comparison questions. Another had a 45-minute session with two interviewers who probed each resume project, asked why technology at Morgan Stanley, and asked about Excel and financial modeling experience.
