Databricks Data Scientist Interview Questions + Guide 2024

Databricks is a rapidly growing data and AI company that empowers data teams to solve the world’s toughest problems using its comprehensive Data Intelligence Platform. From small businesses to over 50% of the Fortune 500, thousands of organizations rely on Databricks for their critical data analytics and AI needs.

Stepping into a Data Scientist role at Databricks means tackling varied challenges, from optimizing machine learning models to developing robust data solutions using Spark. This position demands a strong foundation in data science, especially in areas like machine learning, statistics, and coding in Python and SQL, to effectively drive business decisions and product innovations.

This guide by Interview Query will walk you through Databricks’ interview process for Data Scientist roles, providing tips and insights to help you prepare. Let’s get started!

Submitting Your Application

To start your journey at Databricks, submit an application that aligns your technical skills and enthusiasm with the company’s mission. Whether a recruiter from Databricks reached out to you or you applied directly, it’s essential to tailor your resume and cover letter to the specific job description. Highlight keywords from the job listing, emphasize relevant skills, and underscore your experiences that match the role requirements.

Recruiter/Hiring Manager Call Screening

Once your application catches the eye of Databricks’ hiring team, you will be contacted by a recruiter for an initial screening. This stage, which typically lasts around 30 minutes, will focus on your background, experiences, and interest in the role. Behavioral questions may be asked to gauge your fit for the company culture. At times, the hiring manager might join the call to clarify role-specific queries and discuss technical expectations.

Online Assessment

After passing the initial screening, candidates usually tackle an online assessment. For technical roles like data scientists, expect to encounter coding challenges on platforms like Codesignal, with questions following a standard LeetCode format. This assessment tests your problem-solving abilities, coding proficiency, and efficiency. Do well on both technical and statistical questions to move forward in the process.

Technical Virtual Interview

Successfully completing the online assessment will lead to a virtual technical interview. This 1-hour interview often includes discussing Databricks’ data systems, solving SQL and coding problems, and answering questions about statistics and machine learning fundamentals.

Expect to cover topics like: - SQL questions involving JOIN, HAVING, GROUP BY, and window functions. - Coding exercises in languages like Python, R, or other preferred languages. - Machine learning concepts such as linear regression, random forest, forecasting, hypothesis testing, and probability distributions.

Depending on the seniority of the position, a take-home assignment or case study might be required.

Onsite Interview Rounds

If you pass the virtual technical assessments, you will be invited to participate in an onsite interview loop. The onsite process usually includes multiple rounds focusing on different aspects of data science and machine learning, including coding exercises, technical knowledge, business scenario analysis, and DS fundamentals.

Expect to cover: - Coding exercises, possibly including optimizing a Spark query or solving SQL puzzles. - Discussions about previous project challenges and implementations. - Detailed technical interviews covering machine learning models, statistical tests, and DS fundamentals.

The process can be exhaustive, so be well-prepared by following the preparation guidelines provided by the HR team.

Final Round Rounds

The final round might include discussions around edge cases, follow-up questions on previous answers, coding assessments, and extensive problem-solving tasks. As the process demands high-level preparation, ensure you’re well-versed with a comprehensive understanding of ML/DS concepts.

Quick Tips For Databricks Data Scientist Interviews

• Comprehensive Understanding of ML/DS: Databricks places a high emphasis on having an in-depth knowledge of machine learning and data science principles. Be prepared for follow-up questions and discussions about edge cases.
• SQL Proficiency: Enhance your skills with SQL, particularly advanced concepts like window functions and optimization techniques.
• Time Management During Coding Tests: The interview process includes multiple coding exercises. Practice to solve problems quickly and efficiently, as speed is crucial during these assessments.

Typically, interviews at Databricks vary by role and team, but commonly Data Scientist interviews follow a fairly standardized process across these question topics.

• Develop a function str_map to determine if a one-to-one correspondence exists between characters of two strings at the same positions. Given two strings, string1, and string2, write a function str_map to determine if there exists a one-to-one correspondence (bijection) between the characters of string1 and string2.

• Design three classes: text_editor, moving_text_editor, and smart_text_editor with specific functionalities. Create three classes with the following functionalities:

1. text_editor: Methods to write, delete, and get notes.
2. moving_text_editor: Extends text_editor with a special operation to move the cursor.
3. smart_text_editor: Extends text_editor with a special operation to undo actions.

• Write a function sum_pair_indices to find indices of two integers in an array that add up to a target integer. Given an array and a target integer, write a function sum_pair_indices that returns the indices of two integers in the array that add up to the target integer. Ensure the solution runs in O(n) time.

• Write a query to show the number of users, transactions, and total order amount per month in 2020. Write a SQL query to show the number of users, number of transactions placed, and total order amount per month in the year 2020. Assume the data is from the transactions, products, and users tables.

• Write a SQL query to compute the cumulative sum of sales for each product, sorted by product_id and date. Given a sales table, write a SQL query to compute the cumulative sum of sales for each product, sorted by product_id and date. The cumulative sum includes the price of all purchases of the product on the given date and all previous dates.

• Is this a fair coin? You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if the coin is fair based on this outcome.

• What is the probability that a user has exactly 0 impressions? You have an audience of size A and a limited amount of impressions B. Each impression goes to one user at random. Calculate the probability that a user receives exactly 0 impressions.

• What is the probability that every person has at least 1 impression? Given an audience of size A and a limited amount of impressions B, where each impression goes to one user at random, compute the probability that every person receives at least 1 impression.

• How would you determine if a two-sided coin is biased? You are given a two-sided coin that could be fair or biased. Design a test to figure out if the coin is biased and describe the outcome that would indicate bias.

• What is the probability that a user views more than 10 ads a day? Users view 100 posts a day on a social media website, with each post having a 10% chance of being an ad. Calculate the probability that a user views more than 10 ads a day and approximate this value using the standard normal distribution’s cdf.

• Which Facebook Ads payment option should you choose and why? You have two options for paying Facebook Ads for your e-commerce product growth:

• Pay within 90 days with a 6% fee on the principal.

• Pay within 45 days with a 3% fee on the principal. Determine which option to choose and explain your reasoning.

• What metrics would you use to track the accuracy and validity of a spam classifier for emails? Assume you have built a V1 of a spam classifier for emails. What metrics would you use to evaluate its accuracy and validity?

• How would you build a model to bid on a new unseen keyword using a given dataset? You have a dataset with two columns: keywords and their corresponding bid prices. How would you build a model to bid on a new, unseen keyword?

• How would you build a fraud detection model with a text messaging service for transaction approval? You work at a bank that wants to detect fraud and implement a text messaging service to allow customers to approve or deny transactions flagged as fraudulent. How would you build this model?

• Why has the number of job applicants been decreasing despite stable job postings? You are analyzing a job board where job postings per day have remained stable, but the number of applicants has been decreasing. What could be the reasons for this trend?

• What considerations should be made when testing hundreds of hypotheses with many t-tests? You are conducting numerous t-tests to test various hypotheses. What factors should you consider to ensure the validity and reliability of your results?

• Which Facebook Ads payment option should you choose for e-commerce growth and why? You have two payment options for Facebook Ads: pay within 90 days with a 6% fee or pay within 45 days with a 3% fee. Which option is more advantageous for your e-commerce product growth and why?

What is the average salary for a Data Scientist at Databricks?

Q: What is the interview process like for a Data Scientist at Databricks? The interview process at Databricks typically involves multiple rounds, starting with an online assessment followed by a series of technical interviews. The technical interviews often cover SQL, coding (Python is preferred), and machine learning fundamentals. Expect questions on statistical knowledge, SQL window functions, and machine learning algorithms like linear regression and random forests. The process can be exhaustive, with up to 7-10 interviews over several weeks, including a final round focusing on specific data science domains.

Q: What skills are required to be successful in the Data Scientist role at Databricks? To be successful as a Data Scientist at Databricks, you need robust technical skills in Python and SQL, and experience with distributed data processing systems like Spark. Applicants should have extensive experience in applying data science and machine learning for end-to-end development and deployment of data-driven products. Familiarity with product data science methodologies, an understanding of cloud architecture, and experience with statistical tests and forecasting are also valuable.

Q: What is Databricks’ company culture like? Databricks fosters a culture of innovation, flexibility, and collaboration. The company’s mission is to enable data teams to solve the world’s toughest problems, a goal that is supported by a diverse and inclusive work environment. Employees describe interactions with recruiters and interviewers as friendly and professional, and Databricks places a strong emphasis on mentorship and continuous learning.

Q: What kind of projects would I work on as a Data Scientist at Databricks? As a Data Scientist at Databricks, you will work on projects ranging from segmentation, recommendation systems, and forecasting, to product analytics and churn prediction. You will collaborate closely with engineering, product management, sales, and customer success teams to understand product usage patterns and trends. Your role will also involve developing models for cloud cost forecasting and optimization, as well as building self-serving internal data products.

Q: What benefits does Databricks offer to its employees? Databricks offers a comprehensive benefits package that includes medical, dental, and vision coverage, a 401(k) plan, equity awards, and flexible time off. Additional perks include paid parental leave, family planning support, gym reimbursement, and an annual personal development fund. Databricks also provides resources for mental wellness and business travel accident insurance.

At Databricks, we are relentlessly committed to driving innovation and empowering data teams to tackle the world’s toughest challenges. As a Data Scientist, you will be at the forefront of this mission, shaping the direction of cutting-edge data science projects and making impactful contributions to our robust data and AI platform.

Yes, the interview process can be rigorous and exhaustive, testing your knowledge in areas such as machine learning, statistics, and coding. However, Databricks offers an intellectually stimulating environment with the opportunity to work with passionate professionals and industry-leading technologies. The interview process, despite its length and intensity, is designed to ensure that only the best join the team, and once you’re in, the experience is equally rewarding.

With the benefits of comprehensive health coverage, equity awards, flexible time off, and more, Databricks not only aims to attract top talent but also values its employees’ well-being and professional growth.

So, if you’re ready to push the boundaries of data science and make a meaningful impact, Databricks is the place for you. Prepare thoroughly, stay persistent, and you’ll find that the challenge is well worth the reward. Good luck with your interview journey and welcome to redefining the future of data and AI!