Microsoft Data Scientist Interview Questions + Guide in 2024

With an ever-increasing global revenue of over $245 billion in 2024, Microsoft is among the tech giants that have changed the personal computing and enterprise software world. Sitting on gazillions of terabytes of data, Microsoft has harnessed the power of data science and analytics to drive business growth and innovation.

Data scientists at Microsoft strive to analyze user behavior and enhance user experience by personalizing recommendations, improving search results, and optimizing product designs. Providing insights on business growth, such as sales optimization, customer retention, and supply chain management, is also an essential part of their role.

If you’re looking for detailed information on how the interview process starts and what data science questions you might encounter, you’re in the right place. Let’s dive into the interview process and explore some sample Microsoft data scientist interview questions.

A Microsoft Data Scientist role varies greatly and depends on whichever team you’ve been interviewed Each Microsoft data science job is different and spans from analytics-based roles to more machine learning heavy. As a huge multi-conglomerate corporation, Microsoft has different teams that work on speech and language, artificial intelligence, machine learning infrastructure on Azure, a Data Science consulting for cloud computing, and much more.

Required Skills

Microsoft generally prefers to hire experienced candidates with about a minimum of 2+ years of experience working in data science for a mid-level role. General qualifications are a Ph.D. in a quantitative field and some years of experience in any one of these fields (DNN, NLP, time series, reinforcement learning, network analysis, or causal inference).

• Previous experience in DNN, NLP, time series, reinforcement learning, network analysis, causal inference, or any related areas
• Proficiency in any of the following numerical programming languages (Python/Numpy/Scipy, R, SQL, C#, or Spark)
• Experience with cloud-based architectures such as AWS or Azure

What are the types of data scientists?

Microsoft has a department under engineering called data and applied science. Employees in this department are often placed in teams and go by three main titles: Data scientists, applied scientists, and machine learning engineers. Depending on the team, their functions would include:

• Writing codes to ship models to production.
• Writing codes for machine learning algorithms to be used by other data scientists.
• Working with customers directly or indirectly to resolve technical issues.
• Working on metrics and experimentation.
• Working on product features.
• The ideal candidate for the Microsoft Data and Applied Scientist role is expected to be able to apply a breadth of machine learning tools and analytical techniques to answer a wide range of high-impact business questions and present the insights in a concise and effective manner.

Initial Screen

After submitting your application for the job, the first phone interview may or may not be with a recruiter depending on the seniority level of the role. The hiring manager will often conduct a 30-minute interview first to understand your past experience.

Expect this part of the phone interview to come in two parts. You will be asked about your background and projects, as well as a few technical interview questions. The technical Data Science interview questions will be more theoretical along the lines of explaining how a machine learning concept works or a quick probability or statistical problem.

Examples:

• What’s the difference between lasso and ridge regression?
• How would you explain how a deep learning model works to a business person?
• How would you define a p-value to someone who’s non-technical?

The Technical Screen

After the hiring manager screen, the recruiter will schedule a second more technical screen with a Microsoft data scientist. Generally, this screen is 45 minutes to an hour and designed to test pure technical skills and how well you can code and explain your thought process.

The technical screen consists of around three different questions covering the topics of algorithms, SQL coding, and probability and statistics. Expect questions akin to data structures and algorithms in Python along with data processing type questions.

Examples:

• Given an array of words and a max-width parameter, format the text such that each line has exactly X characters.
• Write a query to randomly sample a row from a table with 100 million rows.
• What’s the probability that you roll at least two 3s when rolling three die?

The Onsite Interview

The onsite interview consists of a full day event from 9 am to 4 pm. You will meet with five different data scientists and go on a lunch interview as well.

Here’s what the interview panel generally looks like:

• Probability and statistics
• Data structures and algorithms
• Modeling and machine learning systems
• Hiring manager and behavioral interview
• Data manipulation
• You’ll also spend 1:1 time with one or two data scientists during a lunch break to learn more about Microsoft and the team. This is usually a one-hour lunch interview in which they’ll let you take a break or talk through what they work on.

The onsite interview will be mostly a combination of all the different technical concepts. Remember to study different model assessment metrics in different circumstances, the bias/variance tradeoff of coefficients under collinearity, open-ended questions about sampling schemes, experimental and ab testing design, explaining p-values to a 5 year old, different concepts of Bayes theorem, and teaching the interviewer a statistical learning technique of your choice.

Another big focus for Microsoft is on communication, since the data science team at Microsoft has partnerships throughout the organization to ensure the team is doing useful work.

You can find many data structures and algorithm questions on Interview Query or Leetcode. It’s also advisable to get a whiteboard to practice writing code on, given how different coding on a whiteboard versus the computer.

Here are a few questions that are often asked in Microsoft Data Scientist Interviews:

1. Tell me a time when your colleagues did not agree with your approach. What did you do to bring them into the conversation and address their concerns?
2. What is your approach to resolving conflict with co-workers or external stakeholders, partially when you don’t really like them?
3. How do you prioritize multiple deadlines? Additionally, how do you stay organized when you have multiple deadlines?
4. What makes you a good fit for our company?
5. Tell me about a project in which you had to clean and organize a large dataset.
6. Jetco, a new North American airline, reportedly has the fastest boarding times. What potential biases could affect this result, and what aspects should be investigated?
7. If your company conducts an AB test on a landing page feature to boost conversion rates and the PM finds a p-value of .04, how would you evaluate the test’s validity?
8. You are testing hundreds of hypotheses with many t-tests. What considerations should be made?
9. What’s the difference between Lasso and Ridge Regression?
10. How would you write a query to randomly sample a row without throttling the database?
11. In an A/B test, how can you check if assignment to the various buckets was truly random?
12. How would you calculate the first touch attribution for each user_id that converted?
13. How would you investigate a decrease in the average number of comments per user from January to March in a new city, despite growing user numbers?
14. What’s the probability that a user gets exactly 0 impressions, and what’s the probability that every user gets at least 1 impression when B impressions are randomly distributed among an audience of size A?
15. A team wants to A/B test changes in a sign-up funnel, like altering a button’s color (red to blue) or position (top to bottom). How would you set up this test?
16. Write a function rain_days to calculate the probability that it will rain on the nth day after today.
17. Given a dataset with missing values, how would you handle them for different types of data (numeric, categorical, time series)? When would you impute, and when would you remove data points?
18. Explain the difference between L1 and L2 regularization. When would you use each, and what are the implications for feature selection?
19. How would you optimize the performance of a machine learning model deployed in production, considering factors like latency, throughput, and accuracy?
20. Given a dataset of user search queries, how would you identify potential product opportunities?

Microsoft expects cultural fit, technical proficiency, and teamwork from its candidates. You must consider these when preparing for your upcoming data scientist interview. Here is a basic guideline on how you may start your Microsoft data scientist interview preparation.

Research the Position

Start by thoroughly reviewing the job description to understand the specific skills and experiences required. Familiarize yourself with the core responsibilities and the types of projects you may be working on. Additionally, research Microsoft’s data science teams, their current projects, and the technologies they use. This knowledge will help you match your expertise to the job description.

Master Core Data Structures and Algorithms

A solid understanding of data structure and algorithms is essential for data scientist technical interviews at Microsoft. Focus on mastering fundamental data structures like arrays, linked lists, trees, and graphs, and practice common algorithms, including sorting, searching, and dynamic programming.

Ultimately, being able to efficiently manipulate and analyze data will be key to solving coding problems and case studies.

Practice Coding Challenges

Coding challenges are a significant part of the Microsoft data scientist interview process. Use our Python coding interview questions to practice a variety of problems. Aim to solve problems that involve data manipulation, algorithmic thinking, and optimization.

Understand Statistical Concepts

As a data scientist, ensure that you have a strong grasp of statistical methods. Review key concepts such as probability distributions, hypothesis testing, regression analysis, and statistical inference. Understanding how to apply these methods to real-world data will help you answer technical questions and perform well in case studies. However, be prepared to also explain your reasoning and interpret statistical results.

Develop a Strong Machine Learning Foundation

Machine learning is central to many data scientist roles. Ensure you’re prepared to tackle questions regarding fundamental ML algorithms, including linear regression, logistic regression, decision trees, support vector machines, and clustering techniques. Additionally, be familiar with model evaluation metrics and techniques for tuning and optimizing models.

Prepare Behavioral Questions

In addition to technical skills, Microsoft evaluates candidates on behavioral aspects. Prepare for questions about your past experiences, teamwork, problem-solving approaches, and leadership skills. Jot down your experiences and structure your responses to provide clear and concise examples.

Practice Mock Interviews

Conducting mock interviews can help you become more comfortable with the interview format, receive constructive feedback, and refine your responses. Participate in our P2P Mock Interview Portal and AI Interviewer to simulate interview environments.

Microsoft expects cultural fit, technical proficiency, and teamwork from its candidates. You must consider these when preparing for your upcoming data scientist interview. Here is a basic guideline on how you may start your Microsoft data scientist interview preparation.

Research the Position

Start by thoroughly reviewing the job description to understand the specific skills and experiences required. Familiarize yourself with the core responsibilities and the types of projects you may be working on. Additionally, research Microsoft’s data science teams, their current projects, and the technologies they use. This knowledge will help you match your expertise to the job description.

Master Core Data Structures and Algorithms

A solid understanding of data structure and algorithms is essential for data scientist technical interviews at Microsoft. Focus on mastering fundamental data structures like arrays, linked lists, trees, and graphs, and practice common algorithms, including sorting, searching, and dynamic programming.

Ultimately, being able to efficiently manipulate and analyze data will be key to solving coding problems and case studies.

Practice Coding Challenges

Coding challenges are a significant part of the Microsoft data scientist interview process. Use our Python coding interview questions to practice a variety of problems. Aim to solve problems that involve data manipulation, algorithmic thinking, and optimization.

Understand Statistical Concepts

As a data scientist, ensure that you have a strong grasp of statistical methods. Review key concepts such as probability distributions, hypothesis testing, regression analysis, and statistical inference. Understanding how to apply these methods to real-world data will help you answer technical questions and perform well in case studies. However, be prepared to also explain your reasoning and interpret statistical results.

Develop a Strong Machine Learning Foundation

Machine learning is central to many data scientist roles. Ensure you’re prepared to tackle questions regarding fundamental ML algorithms, including linear regression, logistic regression, decision trees, support vector machines, and clustering techniques. Additionally, be familiar with model evaluation metrics and techniques for tuning and optimizing models.

Prepare Behavioral Questions

In addition to technical skills, Microsoft evaluates candidates on behavioral aspects. Prepare for questions about your past experiences, teamwork, problem-solving approaches, and leadership skills. Jot down your experiences and structure your responses to provide clear and concise examples.

Practice Mock Interviews

Conducting mock interviews can help you become more comfortable with the interview format, receive constructive feedback, and refine your responses. Participate in our P2P Mock Interview Portal and AI Interviewer to simulate interview environments.

What is the average salary for a Data Scientist role at Microsoft?

On average, the base data scientist salary at Microsoft revolves around $140K and the total compensation reaches up to $348K. The average salary, however, varies based on factors such as experience, location, and specific role. In hindsight, it’s highly competitive with data scientist industry salary standards and offers a lucrative compensation package including base salary, bonuses, and equity.

What other companies are hiring Data Scientists besides Microsoft?

Numerous companies across various industries actively hire Data Scientists, including tech giants like Google, Amazon, and Meta, as well as financial institutions, healthcare organizations, and startups focused on data-driven solutions.

Does Interview Query have job postings for the Microsoft Data Scientist role?

Our Job Board features the latest positions available for candidates, including the data scientist role at Microsoft. Also, feel free to explore other features that’ll help you crack the interview with ease.

Landing a Data Scientist role at Microsoft requires a strong foundation in statistics, machine learning, and coding. Expect rigorous technical interviews focused on problem-solving and data manipulation.

If you’re still in the market, Microsoft also offers a variety of roles in data engineering, machine learning engineering, and data analytics. Explore our Microsoft Main Interview Guide for updated details and additional questions. All the best!