Atlassian Data Scientist interviews, as candidates describe them, mix SQL, Python, A/B testing, ML and case-study rounds with a values or behavioral conversation. Several candidates also report slow, quiet communication between rounds.
$180K
Avg. Base Comp
$277K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Candidates describe an Atlassian Data Scientist loop that pairs hands-on technical work with a lot of reasoning out loud. SQL is the most consistent thread. One candidate got a business question about users who purchased within thirty days of viewing a blog post, and the interviewer pushed back on their joins and aggregations. Two intern candidates also describe SQL joins followed by a business case study.
Other technical content varies by candidate. One reports SQL and A/B testing, a prefix-sum data structures question, and a web-traffic-decline case. Another reports LeetCode-style screens with statistics and ML theory, then an interview asking them to implement a statistical analysis algorithm. A fourth describes SQL and Python fundamentals, followed by an applied round on evaluating LLM systems offline and online, reducing latency, and knowledge distillation. If you are targeting an ML-leaning team, prepare tradeoff discussions rather than definitions.
Online assessments may be broader than practice materials suggest. One intern candidate describes a HackerRank test mixing SQL, statistics and pandas problems, and another describes Python, SQL and data science questions. Practice pandas alongside SQL under time pressure.
Behavioral and values conversations appear in several reports. Candidates describe questions about handling feedback, work style, proudest projects, and the measurable impact of past work. Bring quantified project stories.
All six reports in this set ended without an offer. Several candidates mention long gaps or silence between rounds, so plan your search to allow for that.
Synthesized from 6 candidate reports by our editorial team.
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Real interview reports from people who went through the Atlassian process.
The process at Atlassian felt less like interviewing for a specific role and more like they were evaluating whether you fit their skill set—which actually made it somewhat unpredictable. I went through multiple rounds over several months, starting with a behavioral conversation about what I currently do well, what kind of work energizes me, and what challenges I face. That was followed by a technical round covering SQL and A/B testing concepts, which tested practical experimentation knowledge rather than pure algorithmic depth. There was also a data structures question involving prefix sum logic that required careful explanation of the approach and follow-ups on optimization. One round included a case study—web traffic declining, figure out how you'd approach it—which felt a bit disconnected from my actual domain expertise, but the interviewers seemed to care more about my reasoning process than the specific answer. Then came a principles or values-focused conversation, which felt more like a culture fit assessment. Throughout, feedback was detailed and helped me understand what they were looking for. The overall vibe was that they weren't hiring for a particular open position at the time; they were building a candidate pipeline based on capability. That meant the timeline was long and the outcome uncertain even after all the rounds were done.
Prep tip from this candidate
Prepare for behavioral questions that explore your work style and values, not just accomplishments. Practice SQL and A/B testing scenarios in concrete detail, and be ready to explain algorithmic approaches like prefix sum problems with clear reasoning for trade-offs—Atlassian seems to weight communication and principles as heavily as technical correctness.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
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| Question | |
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| Integer to Roman | |
| Dijkstra implementation | |
| Target Indices | |
| Trial User Segmentation | |
| Target Value Search | |
| Concurrent LLM Serving | |
| Decreasing Subsequent Values | |
| Why Do You Want to Work With Us | |
| LRU Cache 1 | |
| Regularization and Validation | |
| Fraudulent Upvotes | |
| Empty Neighborhoods | |
| Rolling Bank Transactions | |
| Top Three Salaries | |
| Customer Orders | |
| Merge Sorted Lists | |
| Comments Histogram | |
| Closest SAT Scores | |
| Subscription Overlap | |
| First to Six | |
| Monthly Customer Report | |
| Upsell Transactions | |
| Experiment Validity | |
| Download Facts | |
| First Touch Attribution | |
| Employee Salaries (ETL Error) | |
| Minimum Change | |
| Random SQL Sample | |
| Compute Deviation |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an opening recruiter conversation or screen. One describes behavioral questions tied to company values and a discussion of their background, and another describes a recruiter screening before a SQL technical screen. Expect to summarize your experience and why you want the role.
Several candidates report a HackerRank-style assessment or a SQL screen. One intern describes SQL, statistics and about ten Python/pandas problems in one window. Another describes a user-purchase-within-thirty-days SQL problem with follow-up pushback. Timing across sections may matter.
Candidates report varied technical content. Examples include SQL with a data-analysis case, A/B testing, a prefix-sum question, a declining web-traffic case, statistics and ML theory, and applied LLM evaluation and latency questions. The mix likely depends on team and level, so prepare for explaining reasoning, assumptions and tradeoffs.
Some candidates describe a manager conversation, a data science craft round, or a team-matching discussion, including a proudest-ML-project walkthrough with deep follow-ups. These steps are not reported by every candidate, so confirm the sequence with your recruiter.
Candidates report a values-focused or fully behavioral final conversation. Questions mentioned include handling negative feedback, work style, and past projects with measurable business impact. One candidate was braced for company-values questions that did not come up, so prepare both kinds of answers.
