Candidates report Adobe Data Scientist interviews that mix SQL, statistics and Python or algorithm coding with project deep dives, analytics or product cases, and hiring manager and director conversations. The loop varies by team.
$163K
Avg. Base Comp
$230K
Avg. Total Comp
3-5 rounds
Typical Rounds
10-15 days
Process Length
Adobe Data Scientist interviews vary by team, but SQL is the most repeated theme. One candidate who received an offer said SQL appeared in several rounds, alongside a separate round on web analytics and Adobe Analytics. Another reported SQL on user and event tables, covering joins, aggregations, filtering and edge cases.
Statistics and product reasoning come up too. One candidate described a case-style round on measuring success, with follow-ups on assumptions, edge cases and validating results. Another reported probability, statistics and machine learning questions, plus a design round on a production fraud detection system covering pipelines, features, offline and online metrics and A/B testing.
Coding expectations differ. Some candidates report an online assessment mixing statistics and Python. Others describe LeetCode-style medium problems, and one mentioned a random forest implementation. Another candidate described a take-home project and presentation focused on identifying students at risk of failing a class.
Two habits recur across reports: explain your reasoning aloud, and be ready to defend your projects, including methods, alternatives and trade-offs. Ask your recruiter which team you are interviewing for, since the emphasis may shift. Outcomes in these reports were mixed, so treat the loop as team-dependent.
Synthesized from 8 candidate reports by our editorial team.
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| Weekly Aggregation | |
| Threaded Comments | |
| Search Ranking | |
| Google Maps Improvement | |
| Z and t-Tests | |
| Marketing Channel Metrics | |
| Hurdles In Data Projects | |
| Success Measurement | |
| Replace Words with Stems | |
| Testing Price Increase | |
| RAG Strict Source Control | |
| Data Preparation for Imbalanced Data | |
| Overfit Avoidance | |
| Confidence Interval Explanation | |
| Decreasing Subsequent Values | |
| Shortest Path Algorithms | |
| Text Editor With OOP | |
| The Longest Journey | |
| Proof k-Means Converges | |
| Google Docs Drop | |
| POS Subscription Retention | |
| Analyzing Multiple Data Sources | |
| 2nd Highest Salary | |
| Empty Neighborhoods | |
| Rolling Bank Transactions | |
| Top Three Salaries | |
| Comments Histogram | |
| Merge Sorted Lists | |
| Closest SAT Scores |
Synthesized from candidate reports. Individual experiences may vary.
Some candidates report an initial screen. One described an HR screening before a coding round, and another opened with a hiring manager conversation on background fit that was specific to the team and job description. Prepare a concise account of your experience.
Some candidates report an online assessment first. One described statistics plus Python, and another multiple-choice questions on machine learning, deep learning and CS fundamentals. A separate candidate reported a DSA round with two medium coding problems. Whether you get an assessment may depend on the team.
Candidates report a technical round with a data scientist focused on SQL, with Python and machine learning questions in some cases. Reported SQL work includes user and event-style tables, joins, aggregations and edge cases. One candidate also saw statistics on experimentation, metrics and significance. Python data types were mentioned by another candidate.
Rounds here appear to differ by team. Reports include a web analytics and Adobe Analytics round, a business scenario on measuring success, a fraud detection system design, and a take-home project with a presentation. Expect follow-ups on assumptions, validation, trade-offs and how you would communicate results.
Candidates report hiring manager rounds covering SQL and problem solving, technical depth and design thinking. The director round was described as behavioral or non-technical, and in another loop it also covered strategy and stakeholder communication.
