Salesforce data science interviews, as candidates describe them, center on SQL, business reasoning, and clear communication, sometimes with a take-home or presentation. Recruiter follow-up has been uneven.
$188K
Avg. Base Comp
$243K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Candidates describe Salesforce data science interviews as SQL-plus-judgment rather than algorithm drills. One Data Scientist candidate reported joins, aggregations, retention rates, and finding inactive users from activity tables. They were also shown a broken query and asked why numbers were inflated after a join. Another candidate, interviewing for a Data Science Intern role, was tested on table operations and the difference between WHERE and HAVING.
The more distinctive theme is explaining your reasoning out loud. The Data Scientist candidate was asked how to define an active user, why one metric beats another, how seasonality might distort it, and how poor data quality could change the conclusion. They were also asked what to check first when a dashboard metric drops 20%. An interviewer told them to answer as if speaking to a sales manager, so practice short, non-technical explanations.
Some candidates report a larger assignment. The intern candidate received a dataset and case study, built a model, made a slide deck, and presented to the hiring manager. A Lead Decision Scientist candidate reported a SQL and product sense round followed by a take-home that took about seven days.
Recruiter communication was inconsistent in the reports. One Decision Scientist candidate had a SQL round cancelled and then heard nothing, and the Lead candidate heard nothing after submitting the take-home. Confirm next steps and timing in writing. Prepare for behavioral questions about mistakes, incorrect analyses, and ambiguity.
Synthesized from 4 candidate reports by our editorial team.
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Return keys with weighted probabilities
| Question | |
|---|---|
| Month Over Month | |
| Top 3 Users | |
| Rolling Average Steps | |
| Post Composer Drop | |
| Random Forest Explanation | |
| Subscription Retention | |
| Missing Housing Data | |
| Xgboost vs Random Forest | |
| Hurdles In Data Projects | |
| Success Measurement | |
| Most Repetition | |
| Shoe Demand Seasonality | |
| Addressing Data Quality Issues | |
| The Pirate’s Hunt | |
| Three Indexes Adding Zero | |
| Campaign Conversion Gap | |
| Shortest Path Algorithms | |
| Client Solution Pushback | |
| Google Earth Storage | |
| Justify a Neural Network | |
| Your Strengths and Weaknesses | |
| Google Docs Drop | |
| Inactive Users | |
| Weighted Average Sales | |
| Fast Food Database | |
| Reverse List Starting at Index K | |
| Target Whitepages | |
| Analyzing Store Performance | |
| 2nd Highest Salary |
Synthesized from candidate reports. Individual experiences may vary.
One Data Scientist candidate reports a light recruiter call with standard questions such as "Tell me about yourself," "Why data analytics?" and "Why Salesforce?" Prepare a concise story and a specific reason for Salesforce. Timing between application and first contact may vary, as another candidate waited a few weeks.
Candidates report SQL covering joins, aggregations, filtering, retention, inactive users, table operations, and WHERE versus HAVING. One was asked to debug a query whose join inflated the numbers. Interviewers appear to care about how you reason in real time, so narrate your approach.
One Data Scientist candidate reports open-ended prompts: defining an active user, measuring a new feature's success, and investigating a 20% dashboard drop. Follow-ups pushed on metric choice, seasonality, and data quality. Expect to state assumptions and tailor explanations to a non-technical business audience.
Two candidates in related data science roles report assignments. One built a model from a dataset and presented a deck to the hiring manager. Another spent about seven days on a data science take-home. This stage may vary by role and level, so ask the recruiter what to expect and budget time accordingly.
The Data Scientist candidate reports behavioral questions about mistakes, incorrect analyses, and working through ambiguity, with specific follow-ups. Some candidates report cancelled rounds or silence after submitting work, so ask for timelines in writing and follow up politely.
