My Microsoft Data Scientist Interview Preparation Routine

‍Meet Ansh, a mentee at Preplaced who’s been preparing for the data scientist interview at Microsoft. Today, he’s sharing his complete preparation routine here.

Hello! I’m Ansh, and I’ve been in data science for 3 years. Since I began working in the field, I have dreamed of working as a data scientist at Microsoft.

Halfway through this year, I felt like it was officially time for me to make the switch, and I’ve been preparing for the past 2 months for the big move.

One of the best things that happened to me was coming across Preplaced, and my mentor, Hitesh Hinduja.⭐

He’s a Senior Manager in Data Platforms and AI at Microsoft and has 9+ years of experience.

Hitesh helped me fine-tune my resume and pass through the screening processes for the data scientist position at Microsoft.

He’s the mastermind behind my successful 2-month interview prep routine and learning journey.

So, today I will share how I created my preparation routine with fellow aspirants.

If you have questions like what does a Microsoft data scientist do, how to prepare for a Microsoft interview, or how to clear the Microsoft data science interview in 2 months, keep reading.

You’ll find some really valuable nuggets. 

Let’s get this together.🚀

Connect 1:1 with a MAANG mentor today to boost your interview prep. The first session is free.

What Does a Microsoft Data Scientist Do?

Before I tell you about my interview prep routine, here’s a brief rundown of what a data scientist exactly does at Microsoft. 

I think it’s essential to understand the role well to prepare for it.

1. Data investigation: Data Scientists gather and analyse info like customer behaviours and product usage patterns to discover important clues and patterns.

2. Finding insights: Microsoft Data Scientists put the data pieces together to help Microsoft understand their customers better. 

For example, they might find that people from a certain country love a specific Microsoft product more than others.

3. Predictions and recommendations: Like weather forecasters predict the weather, Data Scientists predict which products might become popular. 

They also give recommendations, like suggesting features that make a product more appealing.

4. Machine learning magic: Data Scientists use special tools to teach computers to learn from data. 

This is called machine learning. They build algorithms (think of them as recipes) that help computers make decisions by learning from past data.

5. Visual storytelling: Data Scientists take complex data and turn it into simple graphs and charts that tell a straightforward story. 

For example, they might create a chart to show how many people are using Microsoft's software in different parts of the world.

6. Problem solvers: Companies like Microsoft face challenges every day. Data Scientists help solve these problems by using data. 

For instance, if Microsoft wants to know why a product isn't selling well in a certain region, Data Scientists dive into the data to determine the reasons.

7. Collaborators: Data Scientists don't work alone. They collaborate with other experts like engineers, designers, and business people. 

Together, they use data to make products people love and find useful.

8. Ethical data use: Data Scientists also consider using data responsibly and ethically. They make sure that the data they use is kept private and secure.

In a nutshell, a Microsoft Data Scientist analyses data to uncover valuable insights, predicts trends, and uses this knowledge to help Microsoft make informed decisions and create better products.

How to clear the Microsoft Data Science Interview in 2 months?

How I Created My Complete 2-month Preparation Plan for the Microsoft Data Scientist Interview.

Week 1-2: Understanding the fundamentals

Hitesh and I lay a solid foundation by initially revisiting the data science fundamentals.

These were the building blocks I would need to understand more complex topics later on.

Here's how we did it:

We discussed what data science really is to help me set the right mindset for the upcoming learning journey. 

We discussed how it's all about using data to solve real-world problems.

📌Next, I dove into statistics, a crucial part of data science.

I went through simple statistics concepts like mean, median, and standard deviation, since these would be the tools I'd used to make sense of data.

It never hurts to double down on the basics!

📌Probability was next on the list. 

Hitesh provided me with some scenarios and asked me to calculate probabilities. 

This made the abstract concept more relatable and easier to understand.

📌Linear algebra sounds intimidating at first but is simple. 

I practised basic operations like matrix multiplication and also worked on understanding the importance of visualising data.

Graphs and charts can tell a story that numbers alone can't.

👉My mentor challenged me to think critically about data. He encouraged me to ask questions like "What's the story behind this data?" and "What insights can we gather?" This helped me understand data is like a puzzle waiting to be solved.

📌Throughout the two weeks, I worked on practice problems. These ranged from calculating probabilities to analysing simple datasets. 

Solving these problems gave me a sense of accomplishment and boosted my confidence.

📌Hitesh and I had a reflection session at the end of each week. 

We discussed what I learned, my challenges, and how I could apply these concepts in real-world scenarios.

This reflection helped solidify my understanding.

By the end of these two weeks, I felt much more comfortable with the fundamentals of data science. 

Thanks to my mentor's simple explanations and practical approach, I had a firm base to build upon for the rest of my Microsoft Data Science interview preparation journey.⭐

Weeks 3-4: Mastering data manipulation and visualisation

During these two weeks, I knew that getting a firm grip on data manipulation and visualisation was crucial. 

Hitesh emphasised I needed to understand the basics before diving into complex data tasks. 

We revisited concepts like data types, variables, and basic operations in Python. 

This helped me build a sound foundation to handle data more effectively.

1. Data preprocessing

📍One major challenge in real-world data is its messiness. 

I practised cleaning and processing data using Python's pandas library. 

I focused on handling missing values, removing duplicates, and converting data types. 

This step was crucial because clean data is key for accurate analysis.

2. Advanced data manipulation

Moving forward, I worked on more advanced data manipulation techniques. 

📍I learned about groupby operations, pivot tables, and merging datasets. 

These techniques were like puzzles—I felt more confident managing and shaping data for analysis once I understood them.

3. Data visualisation

Understanding data is one thing, but presenting it visually is another. 

📍I explored data visualisation libraries like Matplotlib and Seaborn. 

I started with simple bar plots and line charts and gradually moved on to more complex visualisations like scatter plots and heatmaps.

4. Telling a story with data

Hitesh stressed the importance of telling a story through data. 

📍We discussed how to choose the right type of visualisation for different data and insights. 

For instance, using a line chart to show trends over time or a bar plot to compare different categories.

5. Microsoft-specific insights

📍What made this preparation particularly effective was incorporating Microsoft-specific insights. 

My mentor shared how Microsoft often deals with vast amounts of user data. 

We explored case studies where data visualisation was pivotal in making informed decisions for product enhancements.

Theory is grand, but practice makes perfect! 

📍So, I challenged myself with real-world datasets. 

I worked on preprocessing the data, performing analysis, and then creating meaningful visualisations. 

This hands-on experience helped me cement what I had learned.

✳️Feedback and refinement: One of the best parts of having a mentor was the feedback loop. 

Hitesh reviewed my data manipulation and visualisations, providing insights on where I could improve. 

This iterative process boosted my skills significantly.

By the end of these two weeks, I not only had a solid grasp of data manipulation and visualisation but also understood how these skills directly applied to Microsoft's data-driven culture. 

Connect 1:1 with Hitesh for a detailed interview prep guidance. The first session is free.✨

Weeks 5-6: Diving deep into machine learning

Step 1: getting the basics straight

I focused on building a solid foundation in the first part of these two weeks. I paid special attention to understanding what machine learning truly meant and its core principles.

Step 2: meeting the algorithms

Next, I worked on different machine-learning algorithms. These are like different recipes for computers to solve problems. 

🔸I focused on learning about decision trees, which help the computer make choices based on data, almost like playing a game of 20 Questions.

Step 3: practising with real data

One exciting thing my mentor did was share real-world examples. 

We looked at data sets and applied these algorithms to them. 

🔸For example, we looked at data about people's age, income, and spending habits to predict if they might buy a certain product. 

Step 4: getting hands-on

Then came the practical part—I extensively focused on coding. 

Hitesh suggested going with Python. 

🔸I started coding simple machine learning models using libraries like scikit-learn, then worked up to more complex models.

Step 5: real-Life problem-solving

Hitesh encouraged tackling real-life problems. 

🔸I worked on projects that predicted housing prices or customer churn. 

This is where I learnt the most.

Step 6: learning from mistakes

Oh, and I made mistakes—plenty of them! But that's where my mentor's wisdom really shone. 

🔸They showed me that mistakes were like stepping stones to learning. 

Instead of feeling frustrated, we dissected my mistakes to understand what went wrong!

Week 7: showcasing problem-solving

During the seventh week of my Microsoft Data Science interview preparation, I focused on honing my problem-solving skills. 

I knew Microsoft values candidates who can tackle real-world challenges creatively, so I wanted to make sure I could show my problem-solving abilities effectively.

Step 1: selecting the right problems

With my mentor's guidance, I identified various sample data science problems relevant to the work Microsoft Data Scientists do. 

💡These problems included scenarios like predicting user behaviour, analysing customer preferences, and improving product recommendations. 

Hitesh helped me understand the key aspects of each problem and why they were important for Microsoft.

Step 2: understanding the problem deeply

Before diving into solutions, I focused on thoroughly understanding each problem. 

💡This involved breaking down the problem statement, identifying the variables and factors involved, and considering the potential challenges I might encounter. 

I learnt to ask questions like "What data would be useful?" and "What assumptions can I make?"

Step 3: brainstorming and researching

I brainstormed different approaches to tackle each problem. 

I went through various algorithms, techniques, and tools that could be applied.

💡My mentor shared insights into how Microsoft typically approaches similar challenges, which helped me align my thinking with their expectations.

Step 4: developing solutions

My mentor and I worked together to implement solutions for the chosen problems. 

💡This involved coding, data manipulation, and applying machine learning techniques. 

Hitesh reviewed my code and provided constructive feedback on how to optimise it for efficiency and readability.

Step 5: communicating the thought process

One of the most valuable lessons from my mentor was the importance of clear communication. 

💡I practised explaining my thought process and solution approach as if presenting to a non-technical audience. 

This skill was essential for interviews, as Microsoft values candidates who can convey complex ideas simply.

Step 6: iterating and improving

Hitesh stressed the iterative nature of problem-solving. 

💡I revisited the problems multiple times, fine-tuning my solutions and considering alternative approaches. 

This helped me understand that getting things right on the first try isn't always the goal—it's about continuous improvement and learning.

✴️Key insights from my mentor:

1. Structured approach: Approach problems systematically, break them down into manageable steps. 

This will prevent you from feeling overwhelmed and help you stay organised.

2. Real-world relevance: Microsoft is interested in how their Data Scientists can apply their skills to real-world challenges the company faces. 

So, focus on problems that align with Microsoft's goals.

3. Storytelling: Presenting your solutions as a coherent story is crucial. 

You can effectively showcase your problem-solving abilities by explaining the problem, the steps taken to solve it, and the outcomes.

⚡Want someone to handhold you through your interview preparation journey until you get your dream job? You can connect with a MAANG mentor here.

Week 8 of my Microsoft data science interview preparation

During Week 8, I knew it was time to fine-tune my interview skills. 

I thought about the different questions that could come up in the interview. 

These questions could be about data analysis, coding challenges, or even how I approach problem-solving. 

My mentor's experience gave me a clear picture of what to expect.

✴️Mock interviews - round one

Hitesh and I kicked things off with a mock interview. 

He took on the role of the interviewer, and I pretended it was the real deal. 

This really helped me get into the right mindset. 

👉He threw questions at me like, "How would you analyse this dataset?" or "Can you explain this complex algorithm in simple terms?" 

I stumbled a bit on some answers, but that was okay! 

Hitesh gave me valuable feedback, pointing out where I could improve. 

He also praised my strengths, which boosted my confidence.

❇️Tailored practice

After the first mock interview, my mentor and I pinpointed the areas where I needed more practice. 

We realised I struggled a bit while explaining my thought process.

So, Hitesh suggested a unique exercise: I would pick a random topic, like making a sandwich, and explain the steps out loud. 

This quirky practice actually helped me become clearer in explaining complex concepts!

✴️Coding challenges

Coding challenges were a part of the Microsoft interview, so I dove into those. 

Hitesh gave me some coding problems to solve on a whiteboard—just like in a real interview. 

I tackled algorithm challenges and data manipulation tasks. 

👉My mentor observed and provided hints when I got stuck. 

This was a game-changer as it taught me to approach problems systematically and think critically.

❇️Behavioural skills

The STAR technique (Situation, Task, Action, Result) helped structure my responses to behavioural questions. 

I practised discussing experiences where I faced challenges, led a team, or dealt with conflicts. 

I learned how to break these experiences down into the STAR format.

With Hitesh’s guidance, I selected a few relevant experiences from my past. 

👉We worked on transforming these experiences into interesting stories. 

He encouraged me to focus on the impact I had, the skills I used, and the lessons I learned.

Microsoft has certain values they look for in candidates. 

👉These values include innovation, collaboration, and customer focus. 

I tailored my stories to highlight how I embodied these values in my past experiences. 

This way, I could show that I'd fit the company culture well.

✴️Feedback loop

We didn't stop with just one mock interview. I practised multiple times. 

After each session, my mentor and I had a feedback discussion. 

He highlighted improvements I'd made and areas where I could still do better. 

It was like having a personal coach cheering me on.

❇️Wrapping up with confidence

By the end of Week 8, I felt remarkably more prepared. 

With my mentor's guidance, I transformed my interview skills. 

I could answer data-related questions smoothly, break down coding challenges confidently, and communicate my ideas clearly.

⭐Key takeaways:

Structured practice: Mock interviews gave me real-world experience and improved my performance.

Feedback: My mentor's feedback was invaluable in identifying my strengths and weaknesses.

Unique exercises: Quirky exercises helped me refine my communication skills and approach problem-solving creatively.

Simulations: Simulating the interview day made me comfortable with the time pressure and boosted my confidence.

👉Thanks to my mentor's help, I walked into the Microsoft Data Science interview room with more than just technical skills—I had the confidence and preparedness to tackle any challenge they threw at me.

You can also choose a MAANG mentor here for personalised interview preparation guidance. The first session is on us.✨

FAQs

These are some questions I had for my mentor before I began preparing. 

I'll jot down the answers for you in case you're wondering about the same things.

1. Does Microsoft hire data scientist freshers?

Microsoft hires data scientist freshers. 

They understand the value of bringing in new talent with fresh perspectives and ideas. 

While having relevant experience or internships can be beneficial, they also consider candidates with a strong educational background and show the required skills in data analysis, statistics, programming, and problem-solving.

2. How many rounds are there in the data science interview at Microsoft?

The data science interview at Microsoft typically comprises several rounds to assess different skills:

Coding round: This round evaluates your coding skills, data manipulation, and problem-solving abilities using languages like Python. 

Expect questions related to data structures, algorithms, and basic programming concepts.

ML round: Your understanding of machine learning concepts will be tested here. 

You might encounter questions about algorithms, model evaluation, and feature engineering.

Previous experience/project round: You'll discuss your previous data science projects, experiences, and how you've tackled challenges in real-world scenarios. 

Be prepared to explain your role and contributions in detail.

System design round: This round assesses your ability to design scalable and efficient data science systems. 

You might be asked about designing a data pipeline or architecture for handling large datasets.

Behavioral/cultural fit round: This round focuses on your soft skills, teamwork, and cultural alignment with Microsoft. 

Be ready to provide examples of how you've collaborated in a team and dealt with challenges.

3. Is the Microsoft interview easy to crack?

The Microsoft data science interview evaluates your skills thoroughly, so it's not necessarily easy. 

However, with proper preparation, understanding of fundamental data science concepts, and practise with coding and problem-solving, you can definitely increase your chances of success. 

Studying the job description, reviewing common data science interview questions, and practising explaining your thought process clearly is important.

4. How long is the Microsoft interview process for a data scientist role?

The interview process can vary, but it usually takes a few weeks to a few months to complete all rounds. 

After the initial contact and discussions, the process typically involves scheduling interviews, completing technical assessments, and final evaluations. 

Timelines can vary based on factors such as the availability of interviewers, candidate schedules, and the urgency of the hiring needs.

5. How much do Microsoft data scientists make?

Microsoft offers competitive compensation to data scientists. 

The salary can vary based on location, experience, education, and role complexity. 

As of 2023, the salary for a data scientist at Microsoft in India can range from around INR 17,50,000 to INR 48,00,000 per year, depending on the abovementioned factors. 

Microsoft also offers various benefits and stock options as part of its compensation package.

It's recommended to check the most recent job listings or salary surveys to get the most accurate and up-to-date information.

Final Words

Reflecting on my two-month journey preparing for the Microsoft Data Science interview, I'm amazed at how much I've grown. 

From mastering technical concepts to acing behavioural questions, every step was a learning experience.

⭐If you're a fellow aspirant for the data scientist position at Microsoft, here are my 2 cents on what can help:

🔹Plan wisely: Divide your time well, focusing on fundamentals, technical skills, and behavioural aspects.

🔹Seek guidance: A mentor's insights are priceless; they provide clarity and valuable feedback.

🔹Double down on practice: Mock interviews and hands-on challenges are your best friends. Embrace them.

🔹Be unique: Incorporate innovative strategies in your routine, whether its quirky exercises or personal anecdotes.

🔹Stay balanced: Remember to balance preparation with relaxation to avoid burnout.

🔹Cultural fit matters: Tailor your stories to the company's values and culture for an edge.

🔹Confidence is key: Believe in your skills and confidently approach the interview.

As you embark on your journey, remember that the preparation process is a growth opportunity. 

Embrace every challenge, learn from setbacks, and celebrate your victories. 

With the right plan and determination, you'll be well-equipped to conquer your Microsoft Data Science interview and open the door to exciting opportunities. 

Best of luck!

Book a free 1:1 mentorship session with a Microsoft mentor to ace your data science interview prep.

Note: Mentee's name has been altered for confidentiality purposes.

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