Four candidates describe Asana's Data Scientist interviews as a mix of recruiter screens, SQL and retention questions, product metrics and experimentation, plus a take-home and a design-style round in some accounts.
$189K
Avg. Base Comp
$250K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Candidates who interviewed for Asana's Data Scientist role describe a process that leans on product analytics: SQL, metric selection and experimentation. Two of the four reports describe a SQL and product analytics round. One of them describes a Week 1 retention query built from an events table, with discussion of joins, CTEs and cohort logic. The other describes a product question about which success metrics to pick for a product, which suggests practicing a primary metric plus guardrails and being able to explain the tradeoffs aloud.
Experimentation comes up repeatedly. Candidates report being asked how to design an A/B test, choose success metrics and interpret results, and one also mentions incrementality measurement. Interviewers reportedly pushed for reasoning, not only a final query or metric, so narrate your assumptions.
The reports differ on format. One candidate describes a Python data manipulation take-home, a phone screen, a HackerRank assessment and a presentation. Another describes four rounds that began with a graph shortest-path algorithm question, followed by a design and integration round and a product owner interview that included a data governance question about a RAG-powered customer care chatbot. That candidate's feedback cited communication with non-technical stakeholders, so practice explaining technical choices in plain language. All four reports ended without an offer, and two mention communication or logistics problems, so confirm the format with your recruiter.
Synthesized from 4 candidate reports by our editorial team.
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Real interview reports from people who went through the Asana process.
The recruiter screen focused on my background and motivation for joining Asana. They asked why I wanted to work at Asana, why I was considering a new opportunity, my experience in product analytics and experimentation, and what types of product decisions I had influenced with data.
The technical round covered SQL, metrics design, and experimentation. One SQL question asked me to calculate Week 1 retention from an events table by finding each user's first activity week and checking whether they were active in the following week. The discussion also covered joins, CTEs, cohort logic, and how to explain the retention metric.
The product analytics portion focused on experimentation and decision-making. I was asked how I would design an A/B test, choose success metrics, and interpret results for a product change. The interviewers pushed for clear reasoning rather than just the final query or metric. I was ultimately rejected.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Asana
How would you calculate daily user retention and rolling activity for Lime's app using login data?
| Question | |
|---|---|
| Assumptions of Linear Regression | |
| Instagram TV Success | |
| Random Bucketing | |
| Friend Requests Down | |
| Reward Experiment | |
| Ranking Metrics | |
| Reservoir Sampling Stream | |
| Podcast Space | |
| Newsfeed Model | |
| Evaluate News | |
| Binary Tree Validation | |
| Client Solution Pushback | |
| A Simpler KNN From Scratch | |
| Your Strengths and Weaknesses | |
| LRU Cache 1 | |
| Disease Testing Probability | |
| 2nd Highest Salary | |
| Empty Neighborhoods | |
| Upsell Transactions | |
| Top Three Salaries | |
| Rolling Bank Transactions | |
| First to Six | |
| Comments Histogram | |
| Merge Sorted Lists | |
| Closest SAT Scores | |
| Last Transaction | |
| Longest Streak Users | |
| Subscription Overlap | |
| Customer Orders |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial recruiter conversation about background and motivation. One was asked why Asana, why they were exploring a new role, and which product decisions their analytics work had influenced. Expect to prepare a concise story linking past experimentation work to business outcomes.
Two candidates describe a technical SQL and product analytics conversation, one of them with a data scientist. One was asked for a Week 1 retention query from an events table, with discussion of joins, CTEs and cohort logic. Another was asked which success metrics to choose for a product. Time may be tight, so practice speaking while you code.
Candidates report product data science case questions covering A/B test design, success metrics, interpreting results and, in one account, incrementality. Interviewers reportedly wanted reasoning that connects analysis to KPIs and product decisions, not just a final number.
Formats differ between reports. One candidate describes a Python data manipulation take-home, a HackerRank assessment and a presentation. Another describes a shortest-path algorithm question, a design and integration round with a RAG chatbot data governance question, and a product owner interview. Confirm your own format with the recruiter.
