Interview Guide May 02
May 022 rounds
As a data scientist working at Microsoft, your role would be to extract insights from data and use them to inform business decisions. You would work with large data sets, using statistical and machine learning techniques to analyze them and identify patterns.
The domains that data scientists work in at Microsoft are quite diverse and include: AI and ML, cloud and enterprise, gaming, search and advertising, and social media. For instance, DSes in the AI and ML domain would work on developing algorithms and models for intelligent applications, such as speech recognition, image recognition, and natural language processing whereas DSes in the gaming domain would work on developing solutions for the gaming industry, such as game optimization, recommendation engines, and player behavior analysis.
There are several different positions available for DSes at Microsoft, including Data Scientist, Applied Scientist, Principal Data Scientist, and Senior Data Scientist.
The examples we’ve outlined above are just a few of the key value-adds that DSes drive at Microsoft. There are plenty of others which we haven’t covered here; but we hope this gives you a flavor of what the role could look like.
How to Apply for a Data Scientist Job at Microsoft?
Take a look at Microsoft’s website and visit their careers page. You'll find plenty of opportunities available, and you can easily apply to roles directly on the site. However, we would highly recommend taking the referral route if you know someone in the company as it increases your chances significantly. Before you hit the apply button, make sure you read the job requirements thoroughly to check if your skills/experiences match against the role’s requirements. Nothing's more frustrating than getting caught off guard during an interview. If you want to increase your chances even more, tailor your resume to align it with the qualifications and experiences listed in the job posting. It'll make you stand out from the rest. If you're not sure how to do that, Prepfully offers a resume review service, where actual recruiters will give you feedback on your resume.
As a part of the Microsoft Data Scientist interview, you’ll will need to go through multiple interview rounds:
1. HR interview - The first round is an HR interview. This session takes place so that the HR team can better understand your background and can help you understand the role. Many candidates reported getting a take home case study or assignment after this round.
2. The final stage of the interview process will comprise multiple different interview rounds. Some of the example rounds are - Behavioral round, Case study round, Algorithmic coding round, ML modeling round and Technical round
Practise more technical questions with our Microsoft Data Scientist Experts→ Book A Mock interview
During the HR interview, the focus is typically on assessing if your abilities align with the position being applied for. This may include informal queries about your experiences and qualifications. The goal of this session is to provide the HR team with a deeper understanding of your background and to assist you in understanding the role. Many candidates reported getting a take home case study or assignment after this round. For instance, one candidate was asked how they improved the model performance in one of their resume projects.
Note that you can expect multiple initial phone screenings such as the HR round.
- Why do you want to join Microsoft?
- Why do you think you will be a good fit for the role?
- What responsibilities do you expect to have from your job at Microsoft?
- Describe a previous project of your choice, frame and solve a problem given a scenario.
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The final stage of the interview process consists of multiple different interview rounds. Some of the rounds you can expect to face are:
- Behavioral Interview: This interview round mostly focuses on conversational questions. The focus of this interview will be on understanding your values and how they align with the company's culture. You can expect questions such as "Tell me about a time when you had to work with a difficult team member," "What are your strengths and weaknesses?" and "What motivates you?".
- Case Study: In this round, you will be given a case study related to the Microsoft business, and you will be expected to analyze the data and provide insights and recommendations. You can expect questions such as "How would you approach this problem?" and "What data would you need to make a decision?"
- Coding round: This round will focus on your coding skills and will include algorithmic, data structures, and Leetcode-type questions. You can also expect some SQL coding in this round.
- System Design round: This round will focus on your ability to design scalable and efficient data pipelines, distributed systems, or other technical systems related to the role. For instance, one candidate was asked “How would you approach designing a system for Bing image search?”
- ML Modeling round: In this round, you will be given a problem related to Machine Learning, and you will be expected to develop a model to solve the problem. For example, one candidate reported that the data given defined a ML problem of imbalance classes and you had to build a ML model with the best performance.
- Technical round: This round will focus on your understanding of probability and statistics, general Data Science concepts, and your ability to apply them to real-world problems. For instance, you can be asked questions related to hypothesis testing, ML models and techniques like bagging and boosting.
Please note that these are just some of the examples of the rounds you could face. There are sometimes additional rounds scheduled which are specific to certain positions.
- What are the ways to minimize residuals on a different statistical model?
- Tell me about the time that you lead a team on your own.
- Code a spiral within a grid.
- Experience with NLP mechanisms, including recommender systems.
- What is a P-value? How would you explain p-value to a non-technical person on the team?
- What are the difficulties faced when you have a model with a lot of layers?
- Explain the key difference between multidimensional and tabular models? Which model is supported by Azure analysis services?
- Do you have techniques that you can illustrate to scoop the data from the set of source tables into the Cube? What is the main calculation and how do you use it in a Cube?
- Can you explain what a Virtual Cluster in Azure Cosmos is? What is Stream in Azure Cosmos?
- Explain all types of joins in SQL (using whiteboard, with pictures ). Having OrgTable(guid,name,office_loc,headcount,org_guid) given org_guid has 1-M relationship with guid, write a query to return team_guid,team_name,org_name,org_guid (to show which org each team belongs to) by leveraging a CTE (Common Table value Expression) recursive query.
- Explain the 5 key (The 5 Vs) factors to consider when building Big Data pipelines. Explain techniques to check for Veracity.
- Explain data structure techniques that can be used to determine an existence of the data element (what to use to determine if a data element exists) in a Big data model?
- Generate 1000 data points normally distributed, with mean=x, and variance=y. Perform summary stat on the data points, explain stat metrics. Create density plot.
- Explain right-skew and left-skew distributions (draw on whiteboard). Where are mean and median values in these cases, compared to symmetrical normal bell shape?
When you are preparing for a Microsoft Data Science interview - we’d recommend the following things to keep in mind:
- Research the company's mission, values, products and services, and current projects. This will help you understand the company's culture and work expectations, and will enable you to answer questions related to the company's mission and values. Check out Microsoft’s values page to prepare better for the interview.
- Practice coding, algorithmic, and data structures questions. Leetcode and other coding websites can help with this. Additionally, it is important to practice SQL coding as well.
- Practice system design questions and develop an understanding of distributed systems, data pipelines, and other technical systems.
- Review statistical concepts like hypothesis testing, p-values, and summary statistics.
- Prepare examples of your experiences that showcase your values, teamwork, problem-solving skills, and adaptability.
- Practice ML modeling questions and understand different ML models and techniques like classification, regression, and imbalance classes.
Responsibilities of a Data Scientist at Microsoft
The responsibilities of a data scientist at Microsoft across roles can broadly be seen as-
- Designing, building, and maintaining services with millions of users, operating in multiple global regions.
- Applying (or developing if necessary) tools and pipelines to efficiently collect, clean, and prepare massive volumes of data for analysis. For example, using technologies such as Azure Data Factory, Azure Databricks, or Azure Synapse Analytics to process large amounts of security telemetry data.
- Continually seeking deeper insights into the performance and scalability of systems to identify areas for improvement. For example, analyzing security telemetry data to identify bottlenecks or performance issues in security products.
- Experimenting and A/B testing key hypotheses to make data-driven decisions. For example, testing the impact of different user interfaces or security policies on customer behavior.
- Improving service reliability, performance, and latency to ensure that security products are always available and performing well.
- Collaborating with product, design, and engineering teams to deliver a delightful experience for customers.
- Collaborating with data analysts to define ML metrics and build feature engineering pipelines and produce ML models for search. For example, working with data analysts to develop models for anomaly detection in security telemetry data or using machine learning to improve the accuracy of security threat detection.
It's important to note that the responsibilities may vary depending on the level of seniority, the specific practice area, the specific industry, and the specific project.
Skills and Qualifications needed for Data Scientists at Microsoft
Here are some skills and qualifications that will help you excel in your Data Science interviews at Microsoft.
- Experience building product/business metrics to measure the effectiveness of products and guide decision-making.
- Solid foundation of statistical modeling, machine learning algorithms, and experimental design to develop accurate predictive models and algorithms. For example, using regression analysis or decision trees to develop predictive models for customer churn or fraud detection.
- Hands-on experience with big data technologies such as Apache Hadoop, Spark, or Azure Data Lake as well as data analytics tools such as R or Python to process and analyze large datasets.
- Excellent data visualization skills to be able to present insights that drive business impact. For example, using tools such as Tableau or Power BI to create interactive dashboards or visualizations to communicate insights effectively.
- Solid ML background and familiarity with NLP and deep learning to build advanced models that can handle complex data types. For example, developing natural language processing (NLP) models to analyze customer feedback or deep learning models for image recognition.
- Demonstrable expertise in dealing with big datasets, including data preparation, cleaning, and processing. For example, using tools such as Apache Spark or Azure Databricks to process and analyze large datasets.
- Knowledge of RDBMS programming to work with relational databases such as SQL Server or MySQL. For example, writing SQL queries to extract data for analysis or building ETL pipelines to transfer data between databases.
- Some of the Microsoft's technology stack includes: Linux on Azure, Java and Ruby microservices deployed as Docker containers, Graph QL, DropWizard, Rails REST APIs, Postgres/CosmosDB/Kafka/RabbitMQ/Redis storage and queuing, Mesos container orchestration, HAProxy-based service mesh, Wavefront metrics, and Azure Data Explorer log aggregation, Azure ML and Synapse for ML model training.
The salary range for a data scientist at Microsoft can vary significantly. Entry-level data scientists at Microsoft may earn a salary between $100,000 and $140,000 per year. Mid-level data scientists with a few years of experience may earn salaries ranging from $130,000 to $180,000 per year. Senior data scientists, or those with extensive experience or leadership roles, can earn salaries well over $200,000 per year, potentially reaching up to $300,000 or more, including bonuses and stock options.
The interview process for a Data Scientist role at Microsoft typically includes 2 primary rounds - a HR interview, and the final interview rounds. The first round is an HR interview. This session takes place so that the HR team can better understand your background and can help you understand the role. Many candidates reported getting a take home case study or assignment after this round. The final stage of the interview process will comprise multiple different interview rounds. Some of the example rounds are - Behavioral round, Case study round, Algorithmic coding round, ML modeling round and Technical round.