Two candidates describe a three-stage Morgan Stanley Data Scientist process: a conversational opening, a technical round on fundamentals, and an HR round on fit. Expect finance awareness alongside regression, Python and SQL.
$152K
Avg. Base Comp
$191K
Avg. Total Comp
3 rounds
Typical Rounds
Not reported
Process Length
Both candidates describe the Morgan Stanley Data Scientist process as three stages that end with an HR conversation. One began with a recruiter phone screen and then a technical interview. The other began with in-person conversations with two managers and then a technical round. Neither described the technical bar as extreme. Fundamentals and clear communication came up in both accounts.
One candidate remembers linear regression as the main technical topic and recommends explaining its assumptions and intuition, not just the formula. The other was asked why multicollinearity matters, what beta is and how to explain it simply, and how forecasting models work. That candidate also reports easy-level live coding, such as palindrome checking, and practical Python and SQL questions on large CSV files that do not fit in memory, garbage collection and pandas pitfalls.
Finance context appears in both accounts. One candidate was asked about their view of the market and how they stay current on financial news. The other heard a question about how depreciation affects the three financial statements. If you apply, prepare a short, natural answer on recent market themes and review basic accounting mechanics.
The second candidate was also asked to walk through personal projects, including LangGraph and agentic data science pipelines, and to explain the design decisions behind them. Be ready to justify tradeoffs in your own work.
Behavioral questions were conversational, for example how friends would describe you. One candidate still found the fit questions tricky because interviewers wanted polished, thoughtful answers, especially around weaker areas. Both candidates report not receiving an offer, so treat these as two data points rather than a fixed format.
Synthesized from 4 candidate reports by our editorial team.
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Real interview reports from people who went through the Morgan Stanley process.
The process started with an in-person first round consisting of two back-to-back 30-minute sessions with different managers. Each conversation began with the standard introductions—tell me about yourself, walk through your resume, explain your interest in the role and Morgan Stanley specifically. Then the questions shifted. They asked about my views on the market right now and what I do to stay current with financial news and trends. It felt less like a quizz and more like they wanted to understand how I think about the world and whether I actually follow markets, which makes sense for a data science role at an investment bank.
The second round was more technical and split across two parts. The first part was live coding for about 30 minutes, where I worked through foundational algorithmic problems—easy-level data structure questions like palindrome checking and some analytical puzzles. It wasn't meant to be a grinding LeetCode hard problem; the bar felt more about demonstrating clean logic and communication. The second part of that round was a deeper dive into my personal projects. I walked through the architecture and implementation of some work I'd done with LangGraph and agentic orchestration, including automated data science pipelines. They were genuinely interested in how I'd built things and what decisions I'd made.
Beyond the coding and project work, I fielded questions on core concepts: why it matters to remove multicollinearity, what beta is and how I'd explain it simply, and how forecasting models work. There were also some practical Python and SQL questions touching on memory management—things like handling large CSV files that don't fit in memory, how garbage collection works in Python, and pandas-specific gotchas. The interviewer seemed knowledgeable and actually tried to be helpful, which made it less adversarial than some interview loops feel.
The final round was an HR discussion centered on cultural fit, professional background, and alignment with the team's goals. Overall, the process felt structured and designed to assess both technical depth and whether I understood the financial context of the role. I didn't receive an offer, but the experience gave me a clear sense of what they're looking for: someone who can code cleanly, think about systems and tradeoffs, and actually engage with markets and finance, not just treat it as an abstract data problem.
Prep tip from this candidate
Go beyond generic data structures practice—brush up on financial concepts like beta and multicollinearity, and be ready to discuss how you'd handle real-world data science constraints like memory limits with large datasets. The market awareness questions signal they want someone engaged with finance, so spend time understanding current trends and how they'd impact forecasting and risk models.
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Featured question at Morgan Stanley
Given two sorted lists, write a function to merge them into one sorted list.
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| Find the Missing Number | |
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| Size of Joins | |
| Get Top N Frequent Words | |
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| Your Strengths and Weaknesses | |
| LRU Cache 1 | |
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Synthesized from candidate reports. Individual experiences may vary.
Candidates report an opening conversation about their background and interest in the role. One had a general recruiter phone screen. The other had two back-to-back 30-minute in-person sessions with managers, who asked about resume, motivation for Morgan Stanley, market views and how they follow financial news.
One candidate reports a technical round centered on linear regression assumptions and intuition. The other reports a technical round with about 30 minutes of easy-level live coding, such as palindromes and analytical puzzles, plus questions on multicollinearity, beta, forecasting, and Python and SQL memory handling.
One candidate reports the technical round included a discussion of personal projects, including LangGraph and agentic orchestration for automated data science pipelines. Interviewers asked about architecture, implementation and the decisions behind it. Be ready to explain the tradeoffs in your own work.
Both candidates describe a final HR-style discussion. One candidate reports it covered cultural fit, professional background and alignment with the team's goals. Prepare thoughtful, structured answers about your background and how you would fit the team.
