Databricks Data Scientist candidates describe a SQL-heavy technical screen, statistics and ML fundamentals, a take-home, and case or design rounds that go deeper than the prep sheet suggests.
$203K
Avg. Base Comp
$256K
Avg. Total Comp
3-6 rounds
Typical Rounds
Not reported
Process Length
Candidates who describe the Databricks Data Scientist interview say the technical bar is higher than the prep material implies. Two of them said the prep sheet was vague or poorly aligned with what was asked, so treat it as a starting point only.
One candidate had a technical screen split into roughly 40 minutes of SQL and 10 minutes of stats/ML. The SQL was the main focus, followed by a probability question and an ML evaluation question. The same candidate later met a very difficult SQL problem in the coding round and an econometrics-heavy design round. Practice SQL under time pressure, along with probability basics.
Another candidate reported three rounds: a CoderPad screen in Python and Pandas, a case interview, and a final conceptual round. The case was more statistical than business-oriented. It covered linear regression assumptions and what happens when they are violated. It also asked whether a random forest would over- or underestimate revenue compared with an ARIMA model, and what goes wrong with regression on 10,000 predictors. A third candidate reported a six-round process that included a take-home, data science and ML fundamentals, system design and behavioral questions.
One Staff-level candidate reported a long loop with a take-home and several technical rounds, including a request to explain the transformer architecture component by component. Reported outcomes were mostly no offer, and one candidate declined an offer. One candidate described months of slow, unclear follow-up, so keep other options open while you interview.
Synthesized from 4 candidate reports by our editorial team.
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Real interview reports from people who went through the Databricks process.
The process at Databricks for the Data Scientist role consisted of three rounds, and I found it to be genuinely challenging—not just in difficulty, but in how misaligned the prep materials were with what they actually tested. My first round was a technical screen where I was given an open-ended coding problem in CoderPad and could choose my language. The interviewer was collaborative and focused on understanding my thought process rather than getting a perfect solution, which I appreciated. I worked through the problem using Python and Pandas, and the interviewer asked me to explain my reasoning at each step. They emphasized conceptual understanding over syntax perfection, though I definitely felt the pressure to demonstrate fluency with common data manipulation libraries.
The second round was where things got noticeably harder. It was a case interview that felt unexpectedly technical—less about business intuition and more about rigorous statistical and machine learning fundamentals. I was asked about the assumptions of linear regression and what happens when they're violated, then given a harder question: would a random forest overestimate or underestimate revenue compared to an ARIMA model when forecasting? That question forced me to think deeply about model behavior and trade-offs, not just recite theory. They also asked about the problem with performing regression when you have 10,000 predictor variables—it wasn't just about naming the issue, but discussing dimensionality reduction strategies and why it matters.
The final round included more conceptual ML and stats questions focused on model evaluation and data processing choices. By this point, I felt I understood what they were looking for: not someone who could memorize definitions, but someone who could reason through statistical assumptions, model behavior, and tradeoffs in a real engineering context. The frustrating part was that the prep sheet provided before the interviews was vague and didn't hint at the depth of technical depth they expected. I walked away without an offer, which stung, but it forced me to recognize where my conceptual understanding of machine learning was still surface-level.
Prep tip from this candidate
Go deep on linear regression assumptions and what happens when they're violated—this came up across multiple rounds. Also prepare to compare different model types (random forests vs ARIMA, etc.) in terms of their inherent biases and forecasting behavior, not just accuracy metrics. The interviews rewarded statistical reasoning over coding syntax, so focus there.
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Topics based on recent interview experiences.
Featured question at Databricks
Write a query to show the number of users, transactions, and total order amount per month in 2020
| Question | |
|---|---|
| Merge Sorted Lists | |
| Impression Reach | |
| Bank Fraud Model | |
| Total Spent on Products | |
| Declining Applicants | |
| Fair Coin | |
| String Mapping | |
| Keyword Bidding | |
| Hurdles In Data Projects | |
| Cumulative Sales By Product | |
| Target Indices | |
| Priority Queue Using Linked List | |
| Spam Classifier | |
| Transformer Encoder Layer | |
| Possibly Biased Coin | |
| Shortest Path Algorithms | |
| Text Editor With OOP | |
| Why Do You Want to Work With Us | |
| Approximate Ad Views | |
| Weighted Average With Missing Dates | |
| Your Strengths and Weaknesses | |
| Delivery Online | |
| Cashflow Interest Projection | |
| Empty Neighborhoods | |
| 2nd Highest Salary | |
| Top Three Salaries | |
| First Touch Attribution | |
| First to Six | |
| Experiment Validity |
Synthesized from candidate reports. Individual experiences may vary.
One candidate describes the initial HR screen as easy and the start of a longer loop. Expect to discuss your background and the role. Two candidates say the prep sheet they received did not reflect the technical depth of later rounds.
Three candidates report a take-home assignment, and one describes it as Python or SQL. It may be a significant part of the evaluation. A Staff-level candidate advises treating it as equally important as the live technical rounds.
One candidate reports a technical screen with about 40 minutes of SQL, plus a probability question and an ML evaluation question. Another reports an open-ended CoderPad problem in Python and Pandas, where the interviewer focused on reasoning over perfect syntax.
Candidates report case or design rounds that go deep into statistical theory. Topics include regression assumptions, random forest versus ARIMA forecasting behavior, high-dimensional regression, econometrics, and data science and ML fundamentals. Be ready to explain tradeoffs, not only definitions.
Reports of later stages vary. One candidate describes a case study presentation and a director chat, another a behavioral round, and another a final conceptual ML and stats round. Expect questions about your past data and AI work, and note that follow-up timing varied widely.
