Morgan Stanley, a premier global financial services firm, is renowned for its comprehensive financial solutions and strategic advisory services. In the data-driven era, Morgan Stanley continues to stand out by leveraging cutting-edge technologies and insights.

As a Data Scientist at Morgan Stanley, you'll venture into a role requiring a robust foundation in statistics, machine learning, and programming languages like Python. The interview process is thorough, featuring multiple rounds that assess your technical proficiency, problem-solving abilities, and cultural fit. Candidates can expect questions covering statistical assumptions, programming intricacies, data modeling techniques, and scenario-based problem-solving. Additionally, you'll engage in HR and hiring manager rounds, with inquiries about your motivation, past projects, and future predictions in technology.

At Interview Query, we provide an in-depth guide to navigate these challenging interviews, ensuring you're well-prepared to succeed at Morgan Stanley.

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Morgan Stanley as a Data Scientist. Whether you were contacted by a Morgan Stanley recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Morgan Stanley Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process. Typical questions might include: - Why Morgan Stanley, and what perspective can you bring? - What motivates you for the job? - Why do you wish to work as a Data Scientist?

The recruiter will also cover initial HR-related details and provide you with information about the subsequent hiring phases.

Technical Online Interview

Successfully navigating the recruiter round will present you with an invitation for the technical online interview. This round will likely involve a mix of technical and behavioral questions. Questions in this stage may revolve around:

• Basics of statistics and programming languages like Python.
• Understanding the assumptions of linear regression.
• Estimation problems, such as estimating a parameter from a given distribution, e.g., "Suppose that you have a sample from the uniform(0,T) distribution. How would you estimate the parameter T?"

This round may also involve questions about your previous projects, the programming languages you used, and a hypothetical coding/algorithm scenario.

Technical Take-home Assignment

Depending on your performance in the initial technical interview, you may be provided with a take-home assignment. This assignment usually involves solving a complex problem or developing a model and might include:

• Building a classification model.
• Solving case studies such as identifying the time taken by a vehicle in traffic assuming given lead times and historical data.

The assignment will test your in-depth knowledge of machine learning algorithms, data processing, and possibly some domain-specific knowledge.

Onsite Interview Rounds

Upon successful completion of the technical assignment, you will be invited for onsite interview rounds. These rounds typically involve interviews with multiple team members, including technical and HR representatives. You can expect:

• Several rounds of technical interviews covering machine learning concepts, coding, and statistics.
• Group interviews focusing on teamwork and problem-solving approaches.
• Behavioral interviews that assess your cultural fit and overall motivation.

Some questions that can arise in these rounds include: - Describe your general process of building a classification model. - What is your experience with specific programming languages and tools? - How would you approach certain tasks or problems in a collaborative environment?

During the onsite, you may also be required to present your take-home assignment or a past project.

Quick Tips for Morgan Stanley Data Scientist Interviews

A few tips for acing your Morgan Stanley interview include:

• Know the Basics: Understand fundamental concepts in statistics, linear regression, and Python programming. Be prepared for in-depth questions on these topics.
• Showcase Your Projects: Be ready to discuss projects you've worked on, the challenges you faced, and the solutions you implemented. Highlight your problem-solving and analytical skills.
• Prepare for Multiple Rounds: Morgan Stanley's interview process can be extensive and involve multiple repetitions of the same questions. Be consistent and deliberate in your answers, and prepare for case studies and problem-solving discussions.

Sign Up on Interview Query for More Tips

Typically, interviews at Morgan Stanley vary by role and team, but commonly Data Scientist interviews follow a fairly standardized process across these question topics.

• How would you design a function to detect anomalies in univariate and bivariate datasets? If given a univariate dataset, how would you design a function to detect anomalies? What if the data is bivariate?

• 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.

• 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?

• How would you explain a p-value to a non-technical person? How would you explain what a p-value is to someone who is not technical?

• 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?

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

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

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

Q: What is the interview process at Morgan Stanley like?

The interview process at Morgan Stanley typically involves multiple rounds, which may include HR interviews, technical evaluations, and phone interviews. It could also include case studies, algebra, calculus questions, and discussions on previous projects. Expect to get grilled on the basics of statistics, Python, and machine learning principles.

Q: What type of questions can I expect in the technical interviews?

In technical interviews, you can expect questions about your programming skills (especially Python), statistics, and data science concepts like linear regression assumptions and model building. Specific questions might also include how to estimate parameters in distributions and discussing any projects you've worked on.

Q: What is the company culture like at Morgan Stanley?

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

Q: How can I prepare for a data science interview at Morgan Stanley?

To prepare for an interview at Morgan Stanley, you should review common interview questions, practice your statistical and programming skills, and be ready to discuss your past projects. Utilize resources like Interview Query for targeted practice and insights into specific data science interview questions.

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

Working as a Data Scientist at Morgan Stanley offers an opportunity to engage with challenging projects and develop your skills in a globally recognized financial institution. Despite some concerns about entry-level perks, the 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. From multiple rounds of technical and HR interviews to case studies and programming questions, candidates are challenged on their statistical knowledge, coding proficiency, and problem-solving skills. The company looks for individuals who can articulate their experiences, demonstrate a solid understanding of basic and advanced concepts in data science, and show their enthusiasm for technology and the future of the industry. Although some candidates found the benefits and insurance to be less favorable, the personable nature of the interviewers and promptness of the recruiting team stood out positively.

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!