Databricks Data Scientist Interview Questions
Interviewing for a Data Scientist role at Databricks means proving you can do applied ML and statistics work at scale on top of Spark and the lakehouse platform. Expect a recruiter screen, a technical screen combining SQL and statistics, a coding round in Python or PySpark, an ML case study or take-home exercise, and onsite rounds on experimentation design and cross-functional communication, since Databricks data scientists often partner closely with product and engineering teams building the platform itself.
Interview Process at a Glance
| Stage | Typical Format | What's Assessed |
|---|---|---|
| Recruiter screen | 20-30 min call | Background, motivation, fit |
| Technical screen | 45-60 min, SQL + stats | Query writing, statistical reasoning |
| Coding round | 45-60 min, Python/PySpark | Data manipulation, distributed computing concepts |
| ML case study / take-home | Varies (live or async) | Modeling approach, experiment design, business framing |
| Onsite - experimentation & stats | 45-60 min | A/B testing, significance, causal reasoning |
| Onsite - cross-functional/behavioral | 45 min | Communication, stakeholder collaboration |
| Hiring manager | 30-45 min | Team fit, leveling |
Sample Databricks Data Scientist Interview Questions
- Write a SQL query to compute weekly active users and week-over-week retention.
- Design an A/B test to measure the impact of a new notebook feature on customer usage.
- How would you detect and handle data skew in a Spark job?
- Explain the bias-variance tradeoff and how you'd apply it to a churn prediction model.
- Walk through how you'd build a feature store for a machine learning pipeline.
- Given noisy click data, how would you determine if a metric change is statistically significant?
- How would you evaluate a recommendation model when you don't have ground-truth labels?
- Write PySpark code to compute a rolling 7-day average from a time series table.
- Describe how you'd design an experiment to test pricing changes for a SaaS product.
- Tell me about a time your analysis changed a product decision.
- How do you communicate a nuanced statistical result to a non-technical stakeholder?
- What's your approach to handling missing data in a large, messy dataset?
FAQs
Is Spark experience required? Not mandatory, but strongly preferred since Databricks' product is built on Spark and interviewers often frame problems in that context.
Is there a take-home assignment? Some teams use a take-home or live coding exercise instead of a separate case study round — ask your recruiter which format your loop uses.
How much SQL versus Python is tested? Expect a roughly even split, with SQL emphasized in the technical screen and Python/PySpark in the coding round.
What's the ML case study round like? You'll typically be given a business problem, such as churn or adoption, and asked to design the modeling and experimentation approach out loud.
How long is the full process? Usually three to five weeks including scheduling for onsite panels.
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