Salesforce · Primly Community

Salesforce data scientist interview (SQL + case + stats): what they actually test

ds_dmitri · 4 replies

went through the Salesforce DS loop in early 2026 for a role on the Einstein Analytics team. figured i'd write this up since there's almost nothing specific out there.

the process had 5 rounds total:

recruiter screen - 30 min, standard fit questions, they'll ask what attracted you to Salesforce and a bit about your current stack. not technical.

SQL round - 60 min with a DS on the team. two problems. first was a window function question involving retention cohorts. second was a self-join to find pairs of users who performed some action within 24 hours of each other. both were doable at medium difficulty. they're not trying to murder you here, they want to see structured thinking and clean readable queries. practice CTEs, window functions, datetime arithmetic.

case/product sense - this one surprised me. it was framed as a business case but was really product-analytics flavored. they gave me a scenario where a key Salesforce CRM metric dropped 15% week over week and asked me to walk through how i'd investigate. think: decompose the metric, check for data pipeline issues first, segment by product/region/user type, then form hypotheses. i had 40 minutes and they pushed me to go fast.

stats + modeling - interview with a senior DS, went deep on A/B testing. they asked how i'd design an experiment where users aren't randomly distributed (the Salesforce CRM has org-level clustering). so SUTVA violations, cluster-based randomization, how to handle variance in that setting. also one ML conceptual question: explain bias-variance tradeoff in a non-textbook way. i talked through it using a real project.

final behavioral round - with a director. STAR-method questions, especially around influencing without authority and navigating ambiguity. nothing exotic.

overall the loop was well-organized and my recruiter was responsive. took about 3 weeks start to finish. offer-or-no-decision came 5 business days after final.

if i had to name one thing to prepare most: the stats round. the experiment design question was genuinely hard and separated people.

4 replies

analyst_ana

the clustering / SUTVA point is so real. i bombed an experiment design question at a different company because i assumed IID and the interviewer just stared at me. did they expect a specific solution or was explaining the problem enough?

ae_andre

they wanted me to propose at least one solution. i went with cluster-based randomization (randomize at the org level for CRM), which they liked. they also probed whether i knew paired/stratified designs as alternatives. knowing the options and trade-offs was the bar.

ml_mike

the metric-investigation case is basically a structured debugging exercise. i prep people on the '4 buckets' framework: data quality, definition change, external factors, product change. sounds obvious but if you do it quickly and systematically you look very sharp.

market_realist

three weeks loop is honestly reasonable by 2026 standards. were they hiring for remote or bay area?