Intuit data scientist candidates report a recruiter screen, SQL and Python screening, a hiring manager conversation, and a final loop built around a take-home or project presentation, with heavy A/B testing and product-metrics questions.
$164K
Avg. Base Comp
$247K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Candidates describe the Intuit data scientist process as broad, and a few themes recur across levels: experimentation and causal inference, SQL and Python screens, and a case or project presentation that panels question closely. One candidate's hiring manager screen was heavy on causal inference and asked about running an A/B test with non-typical user assignments. Another candidate's hiring manager round asked about a recent experiment and what to do when results are mixed.
A senior candidate reports a full QuickBooks A/B test scenario in about 20 minutes. It covered primary, secondary and guardrail metrics, hypotheses, what could go wrong, and sequential versus parallel scaling. Interviewers reportedly dug deeper whenever an answer was vague, so practice walking through an experiment end to end without skipping steps.
Technical screens are reported to be practical. Candidates mention SQL (including building KPIs from a sample dataset), Python string and data-structure problems, a conditional probability question, and a notebook exercise on a provided CSV file. Several reports include ML concept questions, such as justifying F1-score and designing a way to recommend tax products to users. One report asked about building marketing mix models.
Take-homes and presentations appear in several reports, and one candidate describes being expected to spend about a week on the take-home. One candidate was also asked how they applied AI at work. Offers and rejections both appear, so the reports don't point to one deciding factor. Expect the process to differ by level and team.
Synthesized from 15 candidate reports by our editorial team.
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Topics based on recent interview experiences.
| Question | |
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| Size of Joins | |
| Minimum Change | |
| Above Average Product Prices | |
| Youtube Recommendations | |
| Decreasing Payments | |
| Marketing Channel Metrics | |
| Term Frequency | |
| Dijkstra implementation | |
| Hurdles In Data Projects | |
| Spam Classifier | |
| Digitizing Student Test Scores | |
| Running Dog | |
| Non-Normal AB Testing | |
| Offer Matching API Design | |
| Client Solution Pushback | |
| Overfit Avoidance | |
| Approval Drop | |
| Check Matching Parentheses | |
| Scalable Data Pipelines | |
| Why Do You Want to Work With Us | |
| Your Strengths and Weaknesses | |
| Marketing Workflow Optimization | |
| Call Center Resource Management | |
| Analyzing Multiple Data Sources | |
| Empty Neighborhoods | |
| Rolling Bank Transactions | |
| 2nd Highest Salary | |
| Top Three Salaries | |
| Last Transaction |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report starting with a recruiter conversation covering background and role fit. One intern candidate describes a written test with many questions before the technical rounds. Use this call to confirm the team, level and later stages.
Candidates report SQL and Python coding, sometimes with statistics or ML concepts. Reports include easy LeetCode-style string problems, KPI-building queries, a conditional probability question, and a notebook exercise on a CSV file. One candidate's session was spent entirely on coding, and another mentions an AI proctoring tool.
Several candidates describe a hiring manager conversation that digs into resume projects and scenario questions. Reports include discussing a recent experiment, handling mixed results, and causal inference. One candidate found it surprisingly difficult because it expected a heavy experimentation background.
One senior candidate reports a take-home before the final loop covering a prediction model, experiment design, exploratory analysis and a slide deck. That candidate says it was expected to take about a week. Other reports describe a craft demo or presentation as part of the final stage. Time needed likely varies by team and level.
Candidates describe a final stage with a presentation followed by questions and several interviews. Reported topics include past projects, metrics and guardrails, business cases, ML concepts and A/B testing. Format and length differ by candidate, and one reports a long, multi-hour day.
