Databricks is well known for its strong engineering culture and expertise in data lake and Spark technologies. I interviewed for the Data Scientist (Product) role, and the overall process had three main stages: Recruiter Screen → Technical Interview → Case Study.
The entire process took about two weeks.
🧩 1. Recruiter Screen
The first round was a 30-minute call with the recruiter. Most of the questions were standard background and motivation checks:
- Why do you want to join Databricks?
- Which data analysis tools are you most familiar with?
- Have you done any experiment design (A/B testing)?
The key here is to clearly explain the project logic and impact.
For example, I mentioned a project where I optimized a recommendation algorithm and improved the CTR by 15%. The recruiter seemed genuinely interested after hearing that.
🧠 2. Technical Interview
This was the most important round. The interviewer was a Senior Data Scientist, and the session was divided into two parts: coding and statistics + SQL.
Coding Section:
They gave a medium-level pandas question:
Given an event log table containing user_id, event_name, and timestamp,
calculate each user's number of active days and average number of events per day.
This tested proficiency with pandas operations like groupby, resample, and merge.
I’d highly recommend practicing time-series operations in pandas, since Databricks interviews often include them.
Statistics Section:
Two conceptual questions were asked:
- How would you determine whether an experiment is statistically significant?
- What if the p-value is small but the effect size is weak — how would you interpret that?
I answered by emphasizing the business context: significance testing is just a supporting tool, and we should consider effect size, confidence intervals, and practical relevance together. The interviewer seemed satisfied with that reasoning.
💬 3. Case Study
The final round was a product analytics case. The prompt was something like:
You found that some users in the recommendation system have very low exposure.
How would you diagnose the problem?
I walked through my analysis systematically, covering:
data pipeline → exposure bias → sampling bias → A/B test design.
The key is to show your structured problem-solving skills and how you connect data insights to product-level reasoning.
🎯 The “Invisible Boost” Behind Getting a DS Offer
If you’re preparing for Databricks, Meta, Amazon, or other tech company DS roles, you might want to check out Programhelp’s live voice-assist and mock interview coaching — especially if you’re short on time and want a structured way to improve.
Their team has guided students through real OA and VO sessions across major tech firms, mastering the interview logic for each role.
With real-time feedback and voice guidance, they can help you save time, avoid mistakes, and perform confidently during your interviews.
Top comments (0)