Microsoft Data Science Interview Guide [25 questions from 2024]

Even if not a FAANG company working at Microsoft is incredibly prestigious. And being a Data Scientist at Microsoft? Now that’s next level. Trust me I’ve worked at Microsoft, and I know just how hard it is to land a job there. I even wrote an Amazon best-selling book all about it!

In this blog, I’ll share insider tips into the Data Science interview process, and show you 25 Microsoft Data Science Interview questions covering everything from SQL to Machine Learning. Trust me after reading this guide you’ll be ready to destroy the interview, just like I did back in the day.

The Microsoft Data Scientist Interview Process

The interview process at Microsoft typically spans about 4-6 weeks, giving you ample time to showcase your skills across multiple stages. Throughout this period, you’ll go through several rounds of interviews with different members of the data science team, as well as senior leaders. Each stage is designed to evaluate a different aspect of your technical expertise and cultural fit. Here’s a breakdown of the process:

Round 1: Recruiter Call

This is the first step, and while it might seem simple, it’s important to make a good impression. Use this to showcase your communication skills and any soft skills that don’t shine through on your resume.

• 💼 Format: Phone Interview
• ⏰ Length: 30-45 minutes
• 👤 Who: Recruiter or Talent Acquisition
• ❓ Topics: Cultural fit, Experience overview, Role expectations

Round 2: Technical Screening

Now the technical evaluation begins. You’ll need to demonstrate your ability to solve coding problems, along with answering questions about statistics or data manipulation.

• 💼 Format: Virtual or Phone Interview
• ⏰ Length: 45-60 minutes
• 👤 Who: Data Scientist or Engineer
• ❓ Topics: SQL, Python, Statistics, Probability

Insider Tip: Failing to nail the technical section is the easiest way to put you out of the running during the interview, especially when it comes to SQL. There are hundreds of resources right at your fingertips so falling short is no excuse.

The best way to practice for the technical screen is to solve real SQL interview questions asked by Microsoft. Which we covered for you in our blog 9 Microsoft SQL Interview Questions along with an interactive coding pad to help you practice.

Round 3: Onsite (or Virtual Onsite)

This is the most comprehensive round, typically broken into multiple interviews covering coding, problem-solving, and behavioral assessments. Expect to dig deep into your technical expertise and business knowledge.

• 💼 Format: Several Sessions (Coding, Business Cases, Behavioral)
• ⏰ Length: 3-5 hours
• 👤 Who: Data Science Team, Engineers, Managers
• ❓Topics: Technical challenges, Machine learning, Business case solutions, Behavioral questions

Round 4: Final Interview

The last hurdle! This round is a blend of final technical checks and evaluating your cultural fit with the team. Senior leadership will assess your overall readiness for the role.

• 💼 Format: Panel or Individual Sessions
• ⏰ Length: 1-2 hours
• 👤 Who: Senior Managers, Leadership Team
• ❓ Topics: Leadership traits, In-depth technical discussions, Behavioral fit

When will I hear back?

After the final interview round, candidates can expect to hear back from Microsoft within 1-2 weeks. During this time, the hiring team will review your performance across all interview stages and gather feedback from each interviewer. If you're successful, a recruiter will reach out with an offer or next steps.

In some cases, the process may take a bit longer depending on the number of candidates or internal discussions, so don’t be discouraged if it takes a little extra time. If you haven’t heard back after two weeks, it’s a good idea to follow up with your recruiter for an update.

Microsoft Data Science Interview Questions

During the Microsoft Data Science interview, candidates will typically encounter five types of questions, each aimed at assessing different skills

• 🐍 Python and SQL
• 📊 Statistics and Probability
• 🤖 Machine Learning
• 📈 Business Case/Problem-Solving
• 🧠 Behavioral and Culture Fit

Microsoft Python Questions

1. Factorial Formula

Given a number $n$, write a formula that returns $n!$.

In case you forgot the factorial formula, $n! = n * (n-1) * (n-2) * ..... 2 * 1$.

For example, $5! = 5 * 4 * 3 * 2 * 1 = 120$ so we'd return 120.

Assume is $n$ is a non-negative integer.

p.s. if this problem seems too trivial, try the follow-up Microsoft interview problem Factorial Trailing Zeroes

2. Merge Conflicts

Imagine you are working on a code version-control system website, similar to GitHub.

You are given a list of , and each element within the list represents a range of lines which that were changed in a specific pull request.

Your job is to write a function called which returns or , depending on if there is or is not any merge conflict. In this case, a merge conflict means two different pull requests are trying to change the same exact lines.

For example, if you were given the input .

We'd output because there is a merge conflict: two different pull requests trying to change lines between 25 and 40.

Here's another example: say you had the input .

You'd return because there is no merge conflict – none of these pull requests are trying to change the same lines.

Solve this question on our interactive coding platform for FREE!

Microsoft SQL Questions

1. Team Power Users

Write a query to identify the top 2 Power Users who sent the highest number of messages on Microsoft Teams in August 2022. Display the IDs of these 2 users along with the total number of messages they sent. Output the results in descending order based on the count of the messages.

Assumption:

• No two users have sent the same number of messages in August 2022.

Table:

Example Input:

Example Output:

The dataset you are querying against may have different input & output - this is just an example!

2. Mean, Median, Mode

You're given a list of numbers representing the number of emails in the inbox of Microsoft Outlook users. Before the Product Management team can start developing features related to bulk-deleting email or achieving inbox zero, they simply want to find the mean, median, and mode for the emails.

Display the output of mean, median and mode (in this order), with the mean rounded to the nearest integer. It should be assumed that there are no ties for the mode.

Table:

Example Input:

Example Output:

Explanation

The mean is calculated by adding up all the email counts and dividing by the number of users, resulting in a mean of 200 (i.e., (100 + 200 + 300 + 200 + 200) / 5).

The mode is the value that occurs most frequently, which is 200 in this case, since it appears three times, more than any other value.

The median is the middle value of the ordered dataset. When the data is arranged in order from smallest to largest (100, 200, 200, 200, 300), the median is also 200, which separates the lower half from the higher half of the values.

The dataset you are querying against may have different input & output - this is just an example!

Want more practice? Try out these 9 Microsoft SQL Interview Questions.

Microsoft Statistics and Probability Questions

In this section, we’ll delve into essential statistics and probability questions that reflect Microsoft’s emphasis on data-driven decision-making and analytics

1. What is the Central Limit Theorem, and why is it important in statistics?
2. Explain the difference between descriptive and inferential statistics, providing examples of each.
3. How would you interpret a p-value in the context of hypothesis testing?
4. Can you discuss the various types of distributions (e.g., normal, binomial, Poisson) and when to use each?
5. Describe the concepts of Type I and Type II errors in hypothesis testing, along with their implications in decision-making.

Try these 20 Statistics Questions asked in the Data Science Interview!

Microsoft Machine Learning Questions

These questions are tailored to Microsoft’s innovative products and services, focusing on how machine learning can enhance their offerings and drive business solutions.

1. Explain the bias-variance tradeoff in machine learning. How would you address high variance in a model?
2. What are the key differences between bagging and boosting in ensemble learning? When would you use one over the other?
3. How would you handle an imbalanced dataset when training a machine learning model? What techniques can be applied to improve model performance?
4. Can you explain how gradient descent works and how the learning rate affects model training?
5. Describe a scenario where you would use a convolutional neural network (CNN). What type of data is it best suited for?

Want more questions? Try these 70 Machine Learning Interview Questions & Answers.

Microsoft Business Case/Problem-Solving Questions

In this section, we’ll explore critical business cases and problem-solving questions that highlight Microsoft’s focus on leveraging data to drive strategic decisions and improve product offerings.

1. How would you analyze user engagement data to improve the retention rate of a Microsoft product?
2. If you were tasked with optimizing the pricing strategy for Microsoft Office subscriptions, what factors would you consider and how would you approach the analysis?
3. Describe a data-driven approach you would take to identify new market opportunities for Microsoft Azure.
4. How would you evaluate the success of a marketing campaign for a new Xbox game? What metrics would you use?
5. Imagine you are given a dataset of customer feedback for Microsoft Teams. How would you analyze this data to recommend improvements to the product?

BTW a ton of the business case questions are centered around product sense questions, so check out these 13 product sense interview questions!

Microsoft Behavioral Questions

Microsoft's data science interviews often include behavioral questions that center around your past experiences and how they align with Microsoft's core values. Expect questions that delve into your problem-solving abilities, teamwork, adaptability, and how you handle challenges and feedback in a collaborative environment.

1. Describe a challenging project you worked on. What obstacles did you face, and how did you overcome them?
2. Tell me about a time when you had to work with a difficult team member. How did you handle the situation?
3. Can you provide an example of a time when you used data to influence a decision or persuade a stakeholder?
4. Discuss a situation where you had to balance multiple priorities. How did you manage your time and resources?
5. Share an experience where you failed to meet a goal. What did you learn from that experience, and how did you apply it in the future?
6. Describe a time when you had to learn a new tool or technology quickly. How did you approach the learning process?

For more insight into crafting killer answers to behavioral questions, check out our Data Science Behavioral Interview Question Guide and learn all about the STAR methodology.

Preparation Tips for the Microsoft Data Science Interview

Here are three tips for those interviewing for a data science role at Microsoft:

1. 🏢 Understand Microsoft’s Products and Services: Familiarize yourself with Microsoft’s key products, services, and recent innovations. Demonstrating knowledge about how data science can impact these areas will show your genuine interest and ability to contribute effectively.
2. 🛠️ Prepare for Technical Assessments: Brush up on your technical skills in programming languages like Python and SQL, as well as statistical concepts and machine learning algorithms. Practice solving coding challenges and data manipulation problems to build confidence.
3. 🧠 Showcase Your Problem-Solving Approach: During the interview, emphasize your problem-solving process. Think aloud as you work through technical questions, and demonstrate how you break down complex problems into manageable parts, as well as how you derive actionable insights from data.

Best Resources for the Microsoft Data Science Interview

If you're serious about acing the Microsoft Data Science interview, this one blog article ain't gonna cut it. Here are the 7 best resources to study:

1. Ace the Data Science Interview: written by 2 Ex-Facebook employees, this is the go-to resource for Acing the Meta Data Science Interview. The book has 201 real FAANG interview questions, including 11 from Facebook/Meta.
2. DataLemur: 200+ SQL interview questions from Microsoft, and other big-tech companies like Amazon, Google, Meta, Netflix etc.
3. Chip Hyuens Free Machine Learning Interview Book: Chip Huyen's a former Lecturer at Stanford Universit, and has built ML tools at NVIDIA, Snorkel AI, and Netflix. Her eBook on ML Interviews is a great free resource for Data Scientists who are applying to roles
4. 1:1 Mock Data Science Interview: Get 1:1 coaching with me (Nick Singh) – I'm ex-Meta and have helped hundreds land jobs at top companies
5. A/B testing Questions Blog: This guide walks you through how to run consumer experiments, which is a frequent topic due to how important product experimentation & interpreting test results is for Microsoft Product Analytics roles
6. Microsoft Careers Website: Visit this site to learn more about the company culture, values, and available data science roles