Morgan Stanley interview questions

Showing 1–11 of 11 results

• Answer basic probability and statistics questionsMedium

  This problem tests your conceptual understanding of probability distributions, statistical bias in data collection, and the relationship between covariance and linear correlation.

  Morgan StanleyData ScientistMachine LearningOnline Assessment
• Compute minimal cost to merge numbersMedium

  Compute minimal cost to merge numbers evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Morgan StanleySoftware EngineerCoding & AlgorithmsOnline Assessment
• Compute win probability in coin-toss gameEasy

  This question evaluates understanding of probability theory and stochastic processes, focusing on win probability in an alternating coin-toss game and reasoning about pattern occurrence and stopping times in random sequences.

  Morgan StanleyData ScientistCoding & AlgorithmsTechnical Screen
• Count decodings of a digit stringMedium

  Count decodings of a digit string evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Morgan StanleySoftware EngineerCoding & AlgorithmsOnline Assessment
• Describe Your Analysis and Visualization ToolkitMedium

  Tests your ability to articulate an end-to-end data analysis and visualization workflow clearly and concisely for recruiters and hiring managers, connecting technical tool choices directly to business outcomes.

  Morgan StanleyData ScientistData Manipulation (SQL/Python)HR Screen
• Estimate Greeks and hedging PnLMedium

  This question evaluates understanding of option Greeks (delta, gamma, vega), mental approximation of Gaussian probabilities, and the ability to reason about delta-hedged profit-and-loss under Black-Scholes model misspecification, categorized under Software Engineering Fundamentals for a Data Scientist role.

  Morgan StanleyData ScientistSoftware Engineering FundamentalsTechnical Screen
• Explain Your Risk Analytics Work to a RecruiterMedium

  This question tests your ability to translate complex technical risk modeling into a compelling professional narrative that emphasizes business impact, stakeholder collaboration, and personal accountability.

  Morgan StanleyData ScientistBehavioral & LeadershipHR Screen
• Explain forward contracts and optionsEasy

  This question evaluates understanding of financial derivatives—specifically forwards and options—assessing competency in financial instruments, contractual rights and obligations, pricing concepts, and how these concepts inform data-driven modeling for a Data Scientist role.

  Morgan StanleyData ScientistSoftware Engineering FundamentalsTechnical Screen
• Implement Factorial and SquaresMedium

  A Morgan Stanley data scientist HR-screen live-coding warm-up: implement Python `factorial(n)` (iteratively, then explain the recursive version) and `squares(n)` returning the squares of the first n integers. Evaluates clean Python, the iteration-vs-recursion trade-off, time and space complexity analysis, input validation, and edge cases like n = 0 and negative input.

  Morgan StanleyData ScientistCoding & AlgorithmsHR Screen
• Respond to MS technology behavioral interview questionsMedium

  This question evaluates motivation, cultural fit, integrity, judgment, communication, and awareness of technology trends by asking about interest in the organization's technology division, an instance of noticing and addressing a problem, and perspectives on near-term industry developments.

  Morgan StanleySoftware EngineerBehavioral & LeadershipOnsite
• Solve Market-Risk and Probability QuestionsMedium

  Morgan Stanley data-scientist HR-screen quant set covering four sub-questions: design a WTI/Brent crude stress-testing framework using heavy-tailed marginals and a t-copula for joint tail dependence; the variance of a sum of correlated normal variables and when the sum is normal; the Monty Hall switching probability; and the eight-balls one-heavier puzzle in two weighings. It probes both conceptual understanding and practical application of dependence modeling, scenario generation, and probabilistic reasoning.

  Morgan StanleyData ScientistStatistics & MathHR Screen