How to become a Data Scientist at Microsoft?

Get insights on how to ace Data Science interviews and what it takes to secure a position as a Data Scientist in one of the leading tech companies of the world.



"You have to start small, but you have to keep on making these iterative improvements so that you can get to something big that can make an impact."

Aditya Agarwal, Data Scientist at Microsoft

Microsoft is one of the leading tech giants in the world with a trillion-dollar ever-expanding market.

Data Science is indeed one of the most fast-paced fields in today’s technology-driven world and has enough future scopes in various sectors.

Innovations in the field of Data Science have impacted the Business and IT Sectors and escalated their growth rate on a large scale. 

Several aspirants now aim to crack Data Science Interviews in leading tech companies like Microsoft and make an impact through advanced technologies.

Follow the article below as he answers some of the most frequently asked questions by Data Science Aspirants.

Que: Tell us something about your current position and what you work on.

Answer: I work as a Data Scientist at Microsoft.

My work revolves around Bing's People Also Ask Experience, which shows a block of related questions and answers on Bing's search page.

Suppose you search for Machine Learning on, we provide you with interesting questions like -

  • What are some of the top AI trends in 2022?
    • What are some of the cool machine-learning projects that I can do in 2021?

      We work on enabling this personalized user experience in more than 100 languages in 200+ markets globally.

      We work on cutting-edge technologies such as Deep Learning, Machine Learning, Natural Language Processing and AI techniques to enable this experience.

      Que: What are the things aspirants should incorporate in their resumes? 

      Answer: The first and foremost thing is to highlight your past experiences and projects. 

      Mention the contributions you made and the impact that you had on your previous organization.

      For example, instead of writing "I worked on enabling … ", you should write "I worked on enabling ..., which improved developers productivity by 80% …. that led to a revenue increase of about $1 million."

      Do not write terms that you're not aware of.

      Also, do not mention certifications because they don't play an essential role in top tech companies.

      Courses with projects do hold good value when demonstrated with a relevant GitHub page or research work.

      Here is a list of powerful resume tips that will definitely help you get past the screening round.

      Que: What do you suggest for students who have just graduated and might not have much experience? 

      Answer: The right time to start is at the college level, by doing courses that will help you gain some level of understanding of Machine Learning concepts.

      Identify your particular area of interest and do implement some projects on Kaggle

      Once you start implementing, you will understand how modelling works, what are the issues with data processing, and accuracy improvement.

      Try to look for new ways of solving the problem. 

      Increase the complexity of the problem statement such that you can tackle harder and harder problem statements. 

      You will realize you're working on a cool research project which has the scope of a publication. 

      So make the start!

      Que: What is the interview process at Microsoft for Data Scientist?

      Answer: We have five rounds of the interview process. 

      Firstly a simple round to test your ability to write code.

      The next would be a conceptual round.

      When you're applying for a Machine Learning or a Data Scientist role, you're going to get tested on concepts like sequence-to-sequence modelling, RNN, vanishing gradient, overfitting and underfitting.

      You will be asked to elaborate on your problem and expected to answer counter-questions.

      The third round will be based on your previous projects or experiences. 

      In this round, the expectation is to give a detailed overview of what you have worked on in your previous companies, what the problem was and how you solved it.

      For freshers, it could be questions about internships or projects they have worked on as undergraduate students with their professors or some other university or research labs.

      The next round is called the System Design Round where you will be given a real-world use case like that of a movie recommendation system like Netflix.

      You have to provide a blueprint of what Netflix is like, with a set of movies and users interacting over the web.

      You need to elaborate on their task to recommend interesting movies to the right set of users.

      This round tests your thinking, articulating and requirement listing skills that would lead to some sort of a product.

      The last interview round is a managerial round to assess if you are culturally fit.

      That is the usual pattern followed for all interviews here.

      Que: Any specific courses or resources that you would like to mention.

      Answer: I would like to mention two sets of resources -

      • Interviewbit, Geeksforgeeks and Leetcode to prepare yourself algorithmically
        • Machine Learning Courses from Coursera by Professor Andrew Ng

          This will give you a clear idea about your area of interest in the field of Machine learning or Data Science.

          Que: What are the common mistakes you have seen candidates make?

          Answer: One of the common mistakes that candidates make is throwing in terms at the resume that they are not at all aware of.

          Another thing that people miss out on in the interview rounds is not being completely aware of what they worked on in their previous company.

          They do not put a lot of emphasis on the work they did and write only a general overview of broad aspects. 

          To avoid these, focus on keywords that will draw a synergy with the role you are applying for.

          For example, if you are applying for a specialist role in Deep Learning, it's always best to highlight the related skills first instead of haphazardly putting all your technical skills in the resume. 

          Mentioning relevant skills saves space and increases your chances of getting shortlisted.

          Filter out projects that you did 4 or 5 years back as they would have very little impact and would rather look simplistic.

          Avoid spelling mistakes and grammatical or semantic errors on your resume. They create a negative impression in front of recruiters.

          So before applying for the job, have it reviewed by a mentor or your peers.

          Wrapping It Up

          We hope that was insightful! 

          Here's the video version of this blog.

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