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Learning and understanding the Microsoft data science interview questions is the key to landing your dream role in this 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.
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 will cover top 20 Microsoft data science interview questions. In addition, we also share the key skills required for this position, as well as the roles and responsibilities carried out by such individuasl. Further, we take a dive into the various types of data science teams at Microsoft and the interview process adopted by the company.
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
Learning and acquiring these skills will help you clear the Microsoft data science interview questions with ease and land your dream job.
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.
Once you have understood these roles and responsibiliites, it will be easier for your to prepare for and crack the Microsoft data science interview questions.
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).
Also read: What Are Phone Screen Interviews Like at Top Companies?
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.
20 Microsoft Data Science Interview Questions
The following are some of the commonly asked Microsoft data science interview questions:
- Can you explain the difference between a Validation Set and a Test Set?
- Explain cross-validation.
- Differentiate between univariate, bivariate, and multivariate analysis.
- Explain Star Schema.
- What is Cluster Sampling?
- What is Systematic Sampling?
- What are Eigenvectors and Eigenvalues?
- What is Supervised Learning?
- What is Unsupervised learning?
- What does “Naive” mean in a Naive Bayes?
- Explain the SVM algorithm in detail.
- What are the support vectors in SVM?
- What are the different kernels in SVM?
- Explain the Decision Tree algorithm in detail.
- What are Entropy and Information gain in the Decision Tree algorithm?
- Python or R – Which one would you prefer for text analytics?
- Why does data cleaning play a vital role in the analysis?
- Explain a case where a false positive is important than a false negative?
- Explain a case where a false negative important than a false positive?
- 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.
Enrol in interview Kickstart's Data Science Interview Masterclass to learn the tips and tricks to ace the interview. In this course you will learn about the data structure & algorithms and key data science concepts such as SQL programming, probability, distribution, A/B testing, regression, and more.
At Interview Kickstart, you get to learn from and practice mock interviews with industry experts from FAANG and tier-1 tech companies like Microsoft.
FAQs: Microsoft Data Science Interview Questions
Q1. What are common Microsoft Data Science interview questions?
Common Microsoft data science interview questions include topics such as machine learning algorithms, data analysis techniques, and statistical methods. Examples are differences between validation and test sets, and the SVM algorithm.
Q2. How can I prepare for Microsoft Data Science interview questions?
To prepare for Microsoft data science interview questions, focus on understanding core concepts in machine learning, statistics, and data structures. Practicing coding and problem-solving skills in Python or R is also essential.
Q3. What skills are evaluated in Microsoft Data Science interview?
Microsoft data science interviews evaluate analytical skills, coding proficiency, and knowledge of machine learning concepts. Expect to demonstrate your understanding of algorithms and data manipulation techniques.
Q4. Are there behavioral questions in the Microsoft Data Science interview?
Yes, behavioral questions are part of the Microsoft data science interview process. You may be asked to discuss past projects, teamwork experiences, and how you handle challenges in data analysis.
5. How important is real-world experience for Microsoft Data Science interview?
Real-world experience is crucial during the Microsoft data science interview. Candidates with hands-on experience in solving data science problems are often favored by interviewers.
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