Morgan Stanley Data Analyst reports describe resume-led screening, practical Excel analysis, and technical discussions covering SQL, Python, metrics, and data handling.
$147K
Avg. Base Comp
$188K
Avg. Total Comp
2-3 rounds
Typical Rounds
Not reported
Process Length
Morgan Stanley Data Analyst reports point to a process that tests how clearly you can explain prior work and apply practical analytics skills. One candidate described a HireVue stage followed by a manager conversation built around an Excel worksheet and a separate resume-led discussion. The interviewers asked detailed follow-ups on past projects, Excel experience, and financial modeling, so prepare examples with specific decisions, methods, and results.
Technical depth varies by team. SQL fundamentals and data reasoning appeared repeatedly: candidates reported joins, deduplication, writing small data-retrieval functions, query optimization, and questions about Python, pandas, and CSV-related work. A newer first-round account also included BI-tool experience, reconciling different team metrics, and explaining how to deliver a requested metric on a short timeline.
Practice narrating an analysis from start to finish: clarify the metric, describe the data checks, explain your approach, and communicate the result. Be ready for basic finance terminology as well; equities and equity derivatives appeared in one VP prescreen. A balance-scale puzzle was also reported, so concise, structured problem solving may be useful alongside core analytics preparation.
Synthesized from 8 candidate reports by our editorial team.
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Featured question at Morgan Stanley
Find the missing integer from a array of consequtive integers
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|---|---|
| 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 | |
| Compute Deviation | |
| Bagging vs Boosting | |
| Subscription Overlap |
Synthesized from candidate reports. Individual experiences may vary.
One direct Data Analyst report included HireVue behavioral prompts before live conversations, while another began with a VP prescreen. Expect questions about your resume, motivation, past projects, and the tools you have used. Prepare concrete explanations of your contributions and the reasoning behind your work.
A candidate described a live Excel worksheet exercise with surveillance-team managers, including an explanation of their thought process. Another reported questions about BI tools, conflicting metrics across teams, and delivering a requested metric quickly. Practice structuring an analysis and communicating decisions clearly.
Direct Data Analyst reports included SQL joins, deduplication, small functions to retrieve data from a SQL table, Python, pandas, CSV basics, and query optimization. Review the concepts aloud and be prepared to explain how you would apply them to a practical data task. One report also mentioned basic finance terminology and a logic puzzle.
