Morgan Stanley Interview Questions — Page 1

Morgan Stanley·Software Engineer·Technical Phone Screen

Apr 2026

Morgan Stanley quant engineer interview with a brain-melting geometry/probability puzzle that I was not remotely prepared for. The kind of question where you either know the trick or you're just sitting there drawing circles in your head for 20 minutes.

A dog runs around the boundary of a unit circle at speed 4. You're somewhere inside the circle and can move at speed 1. The dog always moves toward whichever point on the boundary you're projected to reach first. Can you escape, meaning reach the boundary at a point where the dog isn't? If so, describe a strategy and prove it works.

“I stared at this for way too long before saying anything useful.”

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Morgan Stanley·Data Scientist·Technical Phone Screen

May 2026

Morgan Stanley Data Scientist interview with a scheduling/greedy algorithm problem. Pretty straightforward coding round but the problem has some nuance worth thinking through carefully.

Given a list of meeting time intervals, each defined by a start and end time, find the maximum number of non-overlapping meetings you can attend. Meetings touching at a boundary (one ends where another starts) are considered non-overlapping and can both be attended.

“Classic interval scheduling problem and I knew it was greedy the second I saw it.”

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Morgan Stanley·Data Scientist·Technical Phone Screen

May 2026

Morgan Stanley data scientist interview with a heavy quant finance slant. Three questions, all centered on Black-Scholes mechanics and hedging PnL, which I was not expecting to be grilled on at this depth for a DS role.

For a vanilla European option under Black-Scholes, how would you mentally estimate delta, gamma, and vega without a calculator?
If you can't remember exact values of the Gaussian CDF, what quick approximations or rules of thumb let you estimate the option Greeks on the fly?
If realized market volatility differs from the constant implied volatility you used to price and delta-hedge an option, what does the PnL of your hedged position look like, and when does it make or lose money?

“This was more doable than it sounds.”

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Morgan Stanley·Data Scientist·Onsite - System Design / Architecture

May 2026

Technical interview for a Data Scientist role at Morgan Stanley, focused almost entirely on RAG system design for financial documents. One long, dense question that branched into six sub-parts. Felt more like a design review than a standard interview.

What is chunking in the context of a RAG pipeline, and why does it matter for retrieval quality?
Walk through the differences between fixed-size chunking, semantic chunking, and parent-child chunking for a corpus of long financial filings.
How does parent-child chunking work in practice? Specifically, how do retrieval and context delivery differ between the child and parent spans?
In what scenarios would you prefer parent-child chunking over simpler approaches, and when would simpler chunking be good enough?
How do you reason about the tradeoffs between retrieval recall, retrieval precision, latency, token cost, answer faithfulness, and citation quality when designing this system?
How would you evaluate this RAG system design both offline, before deployment, and online, once users are interacting with it?

“Easier entry point into the broader question.”

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Morgan Stanley·Data Scientist·Technical Phone Screen

May 2026

Morgan Stanley quant/strats-style technical screen for a Data Scientist role, heavy on derivatives theory and OLS derivations. Two big topic areas back to back with no warmup, which felt pretty intense.

Using a no-arbitrage argument, derive the theoretical fair forward/futures price and show that it equals the spot price compounded at the risk-free rate over the contract's maturity.
What is the intuition behind the futures price formula? Why does it relate to the spot price and the risk-free rate the way it does?
At a high level, how are replication and no-arbitrage ideas used to price simple European options? You don't need to derive a full closed-form solution, just explain the overall approach.
What are the classical assumptions underlying OLS linear regression?
Derive the closed-form OLS estimator by minimizing the sum of squared residuals. Show the main steps and give the final matrix formula.
In simple linear regression with one predictor, write the closed-form slope estimate in terms of sample covariance and variance.
What additional assumptions are needed for the OLS estimator to be the Best Linear Unbiased Estimator, and what do you need on top of that for t-tests and confidence intervals to be valid?

“This is pretty standard if you've seen it before, but I fumbled the actual arbitrage construction under pressure.”

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Morgan Stanley·Software Engineer·Onsite - Behavioral / Leadership

May 2026

Behavioral round for a software engineer role at Morgan Stanley's technology division. Three questions, all pretty standard fit stuff but the integrity one tripped me up more than I expected.

Why are you interested in this team, and what unique perspective do you think you'd bring?
Tell me about a time you noticed something that seemed off, like a process or a result, and walk me through how you handled it.
What technology trends do you see being most significant over the next year, and why would they matter to a firm like this one?

“Prepared something for this but it came out clunky.”

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Morgan Stanley·Software Engineer·Technical Phone Screen

Jun 2026

Technical phone screen for a quant engineer role at Morgan Stanley. The whole thing was basically one long deep-dive into Black-Scholes robustness and delta-hedging PnL under misspecified vol. Dense material and they clearly expected you to derive things on the fly, not just recite formulas.

If you delta-hedge an option using a constant implied vol but the stock actually realizes a different vol, what does the PnL structure look like? Walk through the derivation.
Under what conditions do you make money or lose money on a delta-hedged position, and how does that relate to gamma and the difference between realized and implied variance?
Sketch the result that the total hedging PnL approximates an integral over time of one-half times S-squared times gamma times the difference in squared vols. How do you get there?
Even if realized vol is higher than implied vol on average over the life of the option, can you still end up losing money? Why, and what role does the path of the stock price play?
What are the practical implications of this analysis for how you choose your hedging vol? Should you hedge at implied, realized, or something else?

“This is the core question and I knew it was coming but still fumbled the setup.”

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Morgan Stanley·Data Scientist·Technical Phone Screen

Jun 2026

Finance-heavy technical screen for a Data Scientist role at Morgan Stanley. Two questions, both on derivatives basics. Not what I expected going in but probably should have.

Can you explain what a forward contract is, including the rights and obligations of each party, what the underlying asset and price mean, and when someone would actually use one?
What is an option, how does it differ from a forward contract in terms of obligations, and what are the main reasons someone would use one over the other?

“I knew the definition but fumbled the explanation a bit.”

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Morgan Stanley·Data Scientist·Technical Phone Screen

Jun 2026

Interviewed for a Data Scientist role at Morgan Stanley and got hit with a probability puzzle that felt deceptively simple but absolutely wasn't. The kind of question where you think you see the answer and then realize you've been counting wrong the whole time.

Two players alternate flipping a fair coin, with player A going first. The game ends the moment a head is immediately followed by a tail on the very next flip. The player who flips that tail wins. What is the probability that player A wins?

“I spent way too long trying to brute-force the state space in my head.”

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Morgan Stanley·Software Engineer·Technical Phone Screen

Jun 2026

Morgan Stanley quant engineer interview focused heavily on mental math for Black-Scholes Greeks, no calculator allowed. The whole session was basically: here are some option parameters, now estimate delta, gamma, and vega on the fly and explain your reasoning.

Given a set of option parameters (spot, strike, time-to-expiry, vol, rate), estimate the delta, gamma, and vega of a European option without a calculator. Walk through your approximations and reasoning.
How do you approximate the standard normal CDF and PDF quickly when you don't have access to exact Gaussian quantile tables?
How do the Black-Scholes Greeks scale with each of the input parameters, and how would you use that to sanity-check your estimates by perturbing inputs?

“This was the core of the whole interview.”

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