Microsoft Data Science Interview Questions

Microsoft is a big player in the data science industry. In the cloud computing market, it reigns as one of the biggest service providers. Naturally, Microsoft requires an army of data scientists and data engineers to run its services — it is among the top employers that recruit data scientists and engineers in large numbers.

If you are preparing for a tech interview at Microsoft, check out our interview questions page and salary negotiation ebook to get interview-ready! Also, read Microsoft Interview Guide and How to Get Software Engineering Jobs at Microsoft for specific insights and guidance on Microsoft tech interviews.

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The interview process at Microsoft, like at most Tier-1 tech companies, is challenging. You’re expected to not only be well-versed with core data structures and algorithms but also have a solid knowledge of your domain. In this article, we’ll cover everything you need to know about the data science role at Microsoft. We’ll cover:

  • Skills Required for Data Science Interview at Microsoft
  • Roles and Responsibilities of Data Scientists at Microsoft
  • Types of Data Science Team at Microsoft
  • Microsoft Data Science Interview Process 
  • Microsoft Data Science Interview Questions 

Skills Required for Data Science Interview at Microsoft

Microsoft largely prefers candidates with a minimum of 2 years of experience in data science for a mid-level role. Following are the qualifications required for this role:

  • MS/BS in CS/EE/Applied Mathematics/Statistics/DS/ML or related fields
  • Professional experience working on real-world data science problems
  • Professional experience in machine learning libraries
  • Professional experience in a scripting language like Python, NodeJS, Ruby, or Perl (at least one language)
  • Proficiency in a statically typed language like C#, Java, C, or C++ (at least one language)

In addition to these, the following skills are required to work as a data scientist at Microsoft:

  • Knowledge and experience in software engineering principles, parallel and distributed computing, statistics, machine learning, cloud technologies like Azure, AWS, Google Cloud, IaaS, PaaS, and SaaS.
  • Excellent analytical and problem-solving skills
  • Outstanding communication and collaboration skills
  • Strong research, theory, and algorithm background with the ability to apply knowledge to solve real-world challenges

Roles and Responsibilities of Data Scientists at Microsoft

The roles and responsibilities of data scientists at Microsoft differ based on the teams they’re in. Following are some of the general responsibilities:

1. Applying machine learning techniques, including:

  • Predictive modeling
  • Text and image mining
  • Clustering
  • Anomaly detection
  • Forecasting methods
  • Deep learning 

2. Designing, development, and delivery of machine-learning-enabled solutions:

  • Problem definition
  • Data acquisition
  • Data exploration and visualization
  • Feature engineering
  • Evaluating and comparing metrics

3. Working with different kinds of data:

  • Structured and unstructured data sources
  • Batch and streaming modes
  • Formats such as tabular, image, video, audio, text, and time series

Data scientists also need to collaborate with other teams, such as machine learning, distributed systems, program management, and partner products to plan, execute, and deliver scalable cloud services.

Types of Data Science Team at Microsoft 

There is a data and applied science department under engineering at Microsoft. Data Scientists, Applied Scientists, and Machine Learning Engineers are the three main titles for the engineers in this department who are placed in teams. Different teams have different functions that include:

  • Coding to ship models to production
  • Coding for ML algorithms 
  • Resolving technical issues faced by customers
  • Working on metrics, experiments, and product features

If you’re applying for Microsoft Data and Applied Scientist role, you should be capable of handling high-impact business questions. During the interview, you must apply a breadth of machine learning tools and analytical techniques to answer interview questions in a crisp and efficient way. 

Microsoft Data Science Interview Process 

The interview process for the data scientist role at Microsoft is divided into 3 rounds. Let’s look at what each round covers.

Phone Screen

A hiring manager or recruiter will conduct the phone screen, depending on the seniority of the position. It is typically a 30-minute interview to understand your experience. 

Expect discussions on your background and projects. The interviewer might ask you some technical questions too. The technical questions will be theory-based (on machine learning concepts or a quick probability or statistical problem).

Recommended Reading: How to Prepare for and Crack Phone Screen Interviews at FAANG

Technical Screen

After the initial phone screen with the hiring manager or recruiter, you will have a second round of phone screen; this will be a technical screen with a Microsoft data scientist. This generally lasts for 45- 60 minutes. It aims to test your technical skills in coding and how well you can explain your thought process. 

Recruiters usually ask about three questions in this round, and these are based on algorithms, SQL coding, and probability and statistics. You may expect questions on data structures and algorithms in Python along with data processing type questions.

Onsite Interview

On the day of the onsite interview, you will probably spend the entire day at the Microsoft campus. The interview is spread over 3-5 rounds and will be conducted by different data scientists. You can expect questions on coding, domain, and behavior.

You will also be invited for a lunch interview. During your lunch interview, you will spend time with one or two data scientists, who will talk about Microsoft and the team. This is a good opportunity to get to know the team and ask any questions about the team and work culture.

Microsoft Data Science Interview Questions 

The interview process for a data scientist will include behavioral questions, questions on data structure and algorithms, and some domain-specific questions. In this article, we’ve covered a few interview questions on machine learning and data.

For other tech interview questions, check out the following articles:

Microsoft Data Science Interview Questions:

  1. Can you explain the difference between a Validation Set and a Test Set?
  2. Explain cross-validation.
  3. Differentiate between univariate, bivariate, and multivariate analysis.
  4. Explain Star Schema.
  5. What is Cluster Sampling?
  6. What is Systematic Sampling?
  7. What are Eigenvectors and Eigenvalues?
  8. What is Supervised Learning?
  9. What is Unsupervised learning?
  10. What does “Naive” mean in a Naive Bayes?
  11. Explain the SVM algorithm in detail.
  12. What are the support vectors in SVM?
  13.  What are the different kernels in SVM?
  14. Explain the Decision Tree algorithm in detail.
  15. What are Entropy and Information gain in the Decision Tree algorithm?
  16. Python or R – Which one would you prefer for text analytics?
  17. Why does data cleaning play a vital role in the analysis?
  18. Explain a case where a false positive is important than a false negative?
  19. Explain a case where a false negative important than a false positive?
  20. Explain a case both false positive and false negative are equally important?

How Interview Kickstart Can Help You Crack Microsoft Data Science Interview

Rigorous preparation in the right direction will help you land a job at Microsoft as a data scientist or data engineer. A great way to prepare is practicing with friends or peers who have already cracked such an interview or ex-interviewers from Microsoft. 

At Interview Kickstart, you get to learn from and practice mock interviews with industry experts from FAANG and tier-1 tech companies like Microsoft. 

Intrigued? Join our FREE webinar to learn how you can nail your next interview.

Recommended Reading: “How I landed a big data engineer job amid layoffs and hiring freezes.”

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