Top 24 Morgan Stanley Data Scientist Interview Questions + Guide in 2025

Morgan Stanley is a leading global financial services firm providing investment banking, securities, wealth management, and investment management services.

As a Data Scientist at Morgan Stanley, you will be at the forefront of leveraging data to drive business decisions and enhance the firm’s competitive edge in the financial services industry. Your key responsibilities will include developing and implementing machine learning models, analyzing large datasets to extract actionable insights, and collaborating with cross-functional teams to address complex business problems. Proficiency in statistics and programming (particularly in languages such as Python, R, or C++), as well as a strong understanding of machine learning algorithms, are essential for success in this role. A great fit for this position will possess strong analytical skills, a problem-solving mindset, and the ability to communicate technical concepts to non-technical stakeholders.

This guide on Morgan Stanley data scientist interview questions will help you prepare by highlighting the key competencies and topics the company prioritizes in its data science candidates, ensuring you tackle your interview with confidence and clarity.

The interview process for a Data Scientist role at Morgan Stanley is structured and thorough, designed to assess technical skills and cultural fit within the organization. The process typically unfolds as follows:

1. Initial Phone Interview

The first step is an initial phone interview, which usually lasts 15 to 30 minutes. During this conversation, a recruiter will introduce themselves and discuss your resume highlights, motivations for applying, and your interest in the Data Scientist role. This is also an opportunity for the recruiter to gauge your communication skills and fit for the company culture.

2. Technical Evaluation

Following the initial screening, candidates are often required to complete a technical evaluation. This may be a take-home project or a technical phone interview. The focus here is on your understanding of machine learning concepts, statistical analysis, and programming skills. You may be asked to describe your approach to building models, solve specific problems, or answer questions related to financial data analysis.

3. Group Interview

Candidates who successfully pass the technical evaluation may be invited to a group interview. This stage involves interacting with team members and discussing how you would approach various tasks and challenges relevant to the role. The group interview assesses your collaborative skills and your fit within the team dynamics.

4. Onsite Interviews

The final stage typically involves multiple onsite interviews, including up to eight rounds. These interviews may cover various topics, including case studies, algebra, calculus, and previous implementations of data science projects. You will also be evaluated on your soft skills, leadership experiences, and problem-solving abilities. Expect to engage in discussions that require you to think critically and apply your knowledge to real-world scenarios.

Candidates can expect a friendly and personable atmosphere throughout the interview process, with interviewers eager to learn about your experiences and how you can contribute to the team.

Now, let’s delve into the specific interview questions that candidates have encountered during this process.

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Morgan Stanley. The interview process will assess your technical skills in machine learning, statistics, and programming and your ability to fit within the company culture. Be prepared to discuss your past experiences, problem-solving approaches, and understanding of financial concepts related to data science.

Machine Learning

1. Describe your general process of building a classification model.

This question aims to understand your methodology and thought process in developing machine learning models.

How to Answer

Outline your steps, from data collection and preprocessing to model selection and evaluation. Emphasize the importance of understanding the problem domain and the data.

Example

“I start by defining the problem and understanding the business context. Then, I collect and preprocess the data, ensuring it’s clean and relevant. I select appropriate algorithms based on the data characteristics and evaluate model performance using metrics like accuracy and F1-score. Finally, I iterate on the model based on feedback and results.”

2. How do you handle imbalanced datasets in classification problems?

This question tests your knowledge of techniques to improve model performance on skewed data.

How to Answer

Discuss methods such as resampling techniques, using different evaluation metrics, or applying robust algorithms to class imbalance.

Example

“I address imbalanced datasets using techniques like oversampling the minority class or undersampling the majority class. Additionally, I might employ algorithms like Random Forest or use evaluation metrics such as AUC-ROC to better assess model performance.”

3. Can you explain the concept of overfitting and how to prevent it?

This question assesses your understanding of model generalization.

How to Answer

Define overfitting and discuss strategies to mitigate it, such as cross-validation, regularization, and pruning.

Example

“Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern. To prevent it, I use techniques like cross-validation to ensure the model generalizes well, apply regularization methods, and simplify the model when necessary.”

4. What metrics do you use to evaluate the performance of a regression model?

This question evaluates your knowledge of model evaluation.

How to Answer

Mention common metrics and explain their significance in assessing model performance.

Example

“I typically use metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared to evaluate regression models. Each metric provides different insights into the model’s accuracy and predictive power.”

Statistics & Probability

5. Suppose you have a sample from the uniform(0, T) distribution. How would you estimate the parameter T? Why?

This question tests your statistical reasoning and understanding of estimation techniques.

How to Answer

Explain the maximum likelihood estimation method (MLE) and its relevance in this context.

Example

“To estimate T from a uniform distribution, I would use the maximum value from the sample as my estimate for T, as it is the most likely value that T could take given the uniform distribution properties.”

6. What is the Central Limit Theorem, and why is it important?**

This question assesses your grasp of fundamental statistical concepts.

How to Answer

Define the theorem and discuss its implications for inferential statistics.

Example

“The Central Limit Theorem states that the distribution of the sample mean approaches a normal distribution as the sample size increases, regardless of the original distribution. This is crucial for making inferences about population parameters based on sample statistics.”

7.. How do you determine if a dataset is normally distributed?

This question evaluates your knowledge of statistical tests and visualizations.

How to Answer

Discuss methods such as visual inspection (histograms, Q-Q plots) and statistical tests (Shapiro-Wilk, Kolmogorov-Smirnov).

Example

“I assess normality using visual methods like histograms and Q-Q plots, along with statistical tests like the Shapiro-Wilk test. If the p-value is above a certain threshold, I would conclude that the data does not significantly deviate from normality.”

8. Explain the difference between Type I and Type II errors.

This question tests your understanding of hypothesis testing.

How to Answer

Define both types of errors and their implications in decision-making.

Example

“A Type I error occurs when we reject a true null hypothesis, while a Type II error happens when we fail to reject a false null hypothesis. Understanding these errors is crucial for evaluating the reliability of statistical tests and making informed decisions.”

Programming & Technical Skills

9. What is encapsulation in C++?

This question assesses your understanding of object-oriented programming principles.

How to Answer

Define encapsulation and its significance in software development.

Example

“Encapsulation in C++ is the bundling of data and methods that operate on that data within a single unit or class. It helps protect an object’s internal state and restricts direct access to some of its components, promoting modularity and maintainability.”

10. What is a smart pointer in C++?

This question tests your knowledge of memory management in C++.

How to Answer

Explain what smart pointers are and what their advantages are over traditional pointers.

Example

“A smart pointer is an object that acts like a pointer but provides automatic memory management. Smart pointers, such as std::unique_ptr and std::shared_ptr, help prevent memory leaks and dangling pointers by automatically deallocating memory when it is no longer needed.”

11. What is a template in C++?

This question evaluates your understanding of generic programming.

How to Answer

Define templates and their purpose in C++ programming.

Example

“A template in C++ allows functions and classes to operate with generic types. This enables code reusability and type safety, as the same function or class can work with different data types without needing multiple implementations.”

12. Can you describe a project where you implemented a machine-learning model? What challenges did you face?

This question assesses your practical experience and problem-solving skills.

How to Answer

Discuss a specific project, the challenges encountered, and how you overcame them.

Example

“In a recent project, I developed a predictive model for customer churn. One challenge was dealing with missing data, which I addressed by implementing imputation techniques. Additionally, I faced issues with model interpretability, which I resolved by using SHAP values to explain predictions to stakeholders.”

13. How would you design a function to detect anomalies in univariate and bivariate datasets?

How would you design a function to detect anomalies if given a univariate dataset? What if the data is bivariate?

14. What are the drawbacks of the given student test score data layouts?

Assume you have data on student test scores in two layouts. What are the drawbacks of these layouts? What formatting changes would you make for better analysis? Describe common problems in “messy” datasets.

15. What is the expected churn rate in March for customers who bought subscriptions since January 1st?

You noticed that 10% of customers who bought subscriptions in January 2020 canceled before February 1st. Assuming uniform new customer acquisition and a 20% month-over-month decrease in churn, what is the expected churn rate in March for all customers who bought the product since January 1st?

16. How would you explain a p-value to a non-technical person?

How would you explain a p-value to someone who is not technical?

17. What are Z and t-tests, and when should you use each?

What are the Z and t-tests? What are they used for? What is the difference between them? When should you use one over the other?

18. Write a Python function max_profit to find the maximum profit from at most two buy/sell transactions on stock prices.

Write a Python function called max_profit that takes a list of integers, where the i-th integer represents the price of a given stock on day i and returns the maximum profit you can achieve by buying and selling the stock. You may complete, at most, two complete buy/sell transactions to maximize profits on a stock.

19. What are the Z and t-tests, and when should you use each?

Explain the purpose and differences between Z and t-tests. Describe scenarios where one test is preferred over the other.

20. How would you reformat student test score data for better analysis?

Given two datasets of student test scores, identify drawbacks in their current format. Suggest formatting changes and discuss common issues in “messy” datasets.

21. What metrics would you use to evaluate the value of marketing channels?

Given data on marketing channels and costs for a B2B analytics company, identify key metrics to determine the value of each marketing channel.

22. How would you determine the next partner card using customer spending data?

With access to customer spending data, outline a method to identify the best partner for a new credit card offering.

23. How would you investigate if an email campaign led to increased conversion rates?

Analyze a scenario where a new email campaign coincides with an increase in conversion rates. Determine how to verify if the campaign caused the increase or if other factors were involved.

24. How do sentiment analysis models work, and how are they trained?

To perform sentiment analysis on an Amazon customer feedback dataset, you must convert raw text data into numerical vectors. Explain the process of how these models work and how they are trained.

Here are some tips to help you excel in your interview.

Understand the Interview Structure

Morgan Stanley’s interview process often includes multiple stages, such as phone interviews, technical evaluations, and group interviews. Familiarize yourself with this structure and prepare accordingly. Expect a blend of technical questions, case studies, and discussions about your previous experiences. Knowing what to anticipate will help you feel more comfortable and confident during each stage.

Prepare for Technical Questions

As a Data Scientist, you will likely face questions related to machine learning, statistics, and programming. Brush your knowledge of classification models, regression techniques, and statistical concepts. Be ready to explain your thought process in building models and solving problems. Practice articulating your approach to technical challenges, as this will demonstrate your analytical skills and problem-solving abilities.

Showcase Your Financial Acumen

Given Morgan Stanley’s focus on finance, be prepared to answer questions that bridge data science and financial concepts. Familiarize yourself with financial metrics and how data science can be applied to financial analysis. This will show your technical expertise and understanding of the industry, making you a more attractive candidate.

Emphasize Soft Skills and Cultural Fit

Morgan Stanley values personable and friendly interactions throughout the interview process. Be prepared to discuss your teamwork experiences, leadership roles, and how you handle challenges. Demonstrating your ability to collaborate and communicate effectively will resonate well with the interviewers looking for candidates who can thrive in their team-oriented environment.

Engage with the Interviewers

You may be asked how you would approach specific tasks or problems during group interviews. Use this opportunity to engage with your interviewers by asking clarifying questions and discussing your thought process. This showcases your analytical skills and your ability to work collaboratively and think critically in a team setting.

Reflect on Your Motivation

Be ready to discuss your motivation for pursuing a career as a Data Scientist at Morgan Stanley. Articulate why you find the role interesting and how it aligns with your career goals. This personal connection to the role will help you stand out and demonstrate your genuine interest in the position.

Practice, Practice, Practice

Finally, practice is key. Conduct mock interviews with friends or mentors to refine your responses and get comfortable with the interview format. Focus on technical and behavioral questions, and seek feedback to improve your delivery. The more you practice, the more confident you will feel during the interview.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Data Scientist role at Morgan Stanley. Good luck!

What is the average salary for a Data Scientist at Morgan Stanley?

What is the company culture like at Morgan Stanley?

Morgan Stanley offers a professional environment where team members are personable and recruiting staff quickly responds. However, some employee feedback suggests that the perks, particularly insurance, may not be as competitive for entry-level employees.

Why should I consider a Data Scientist role at Morgan Stanley?

Working as a Data Scientist at Morgan Stanley allows you to engage with challenging projects and develop your skills in a globally recognized financial institution. Despite some concerns about entry-level perks, professional growth and exposure to data-driven decision-making can be highly rewarding.

Navigating the interview process for a Data Scientist position at Morgan Stanley is a comprehensive journey that touches on various important aspects of the role.

If you want more insights about the company, check out our main Morgan Stanley Interview Guide, where we have covered many interview questions that could be asked. At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every Morgan Stanley interview challenge.

Good luck with your interview!