Morgan Stanley Data Scientist interview typically runs 3 rounds: recruiter phone screen, technical interview, HR round. It usually takes a few weeks and is notably fit-heavy and conversational.
$165K
Avg. Base Comp
$198K
Avg. Total Comp
3
Typical Rounds
2-4 weeks
Process Length
We've seen Morgan Stanley evaluate data scientists less like pure model builders and more like people who can explain sound judgment in a finance setting. The strongest signal from candidate experiences is that fundamentals matter more than flash: one candidate specifically called out linear regression assumptions and basic intuition as the centerpiece of the technical conversation, while another finance-style prompt about depreciation and the three financial statements shows they still expect comfort with business mechanics, not just statistics.
A recurring theme is that the interviewers are listening for polish and clarity as much as correctness. Multiple candidates reported conversational fit questions, including how friends would describe them, which tells us they are checking whether someone can represent the firm well with clients and internal stakeholders. That makes the non-obvious challenge here the same one we hear from candidates across finance roles: can you explain a technical idea cleanly, stay composed under personal questions, and show you understand the downstream business impact? The process felt fair and manageable, but it rewarded candidates who could connect their answers to judgment, communication, and practical finance awareness rather than trying to overcomplicate the role.
Synthetized from 1 candidates reports by our editorial team.
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Featured question at Morgan Stanley
Find the missing integer from a array of consequtive integers
| Question | |
|---|---|
| Maximum Profit | |
| Size of Joins | |
| Cyclic Detection | |
| Sort Strings | |
| Assumptions of Linear Regression | |
| Coin Dispenser | |
| Implementing the Fibonacci Sequence in Three Different Methods | |
| Last Element of a Singly Linked List | |
| Shortest Path Algorithms | |
| Why Do You Want to Work With Us | |
| Your Strengths and Weaknesses | |
| LRU Cache 1 | |
| Feedback Sentiment Analysis | |
| 2nd Highest Salary | |
| Empty Neighborhoods | |
| Merge Sorted Lists | |
| Rolling Bank Transactions | |
| Comments Histogram | |
| Employee Salaries | |
| Closest SAT Scores | |
| Top Three Salaries | |
| Subscription Overlap | |
| Experiment Validity | |
| Cumulative Distribution | |
| Compute Deviation | |
| Prime to N | |
| Bagging vs Boosting | |
| Last Transaction | |
| String Shift |
Synthesized from candidate reports. Individual experiences may vary.
The process starts with a general recruiter conversation to cover background, interest in the role, and basic fit. This stage is conversational and helps set expectations for the rest of the interview loop.
The technical round focuses on fundamentals rather than advanced coding or case-style problems. Candidates should be ready to discuss linear regression in depth, including assumptions and intuition, and may also get finance/accounting basics such as how depreciation affects the three financial statements.
The final round is an HR interview centered heavily on behavioral and fit questions. Interviewers may ask polished, reflective questions like how your friends would describe you, and they look for clear communication, self-awareness, and overall cultural fit.