Atlassian Data Scientist Interview Questions

Atlassian·Data Scientist·Technical Phone Screen

Jul 2026

SQL question for a Data Scientist role at Atlassian. One question, reasonably tricky, the kind where you think you've got it and then realize there's a wrinkle you almost missed.

Given a team table and a game table, write a single SQL query to find the team name of the 2023 championship winner. The winner could be stored as either team_id or opponent_team_id, there are no ties assumed, but if one exists in the data you should break it by higher score. No hard-coded IDs allowed.

“The part that almost got me was the bidirectional storage.”

View Post

Atlassian·Data Scientist·Technical Phone Screen

Jul 2026

Atlassian Data Scientist interview with a pretty brutal algorithmic question that felt more like a competitive programming contest than anything I'd call data science. One question, lots of depth required, and I'm still not sure I nailed the proof parts.

Given an array of n integers and an integer k, place k cluster centers on the real line to minimize the maximum L1 distance from any point to its nearest center. Design an algorithm faster than O(nk), prove correctness, handle large coordinates, and reconstruct the actual center positions.

“This one hit different.”

View Post

Atlassian·Data Scientist·Technical Phone Screen

Jun 2026

SQL heavy technical screen for a Data Scientist role at Atlassian. One question, but it had a lot of layers: bidirectional game data, window functions, deterministic tie-breaking. The kind of problem where you think you have it and then realize you forgot to union the opponent's perspective.

Given a games table where each row represents one team's perspective in a matchup, write a SQL query that treats every game from both teams' viewpoints, then returns each team's top 3 highest-scoring games for the 2024 season. Include team name, opponent name, team score, and a rank column. Break ties by score descending, then date descending, then game_id ascending.

“The union step is where I almost tripped up.”

View Post

Atlassian·Data Scientist·Technical Phone Screen

Jun 2026

Atlassian data science interview with a classic metric drop scenario. One meaty case question that basically asked you to think out loud about a checkout rate problem under time pressure. Felt more like a business consulting exercise than a pure stats interview.

Checkout completion rate dropped 7% yesterday while overall traffic held steady. Walk through how you'd diagnose the root cause, what analyses you'd run, and how you'd prioritize fixes.

“I started with segmentation which felt right, but I jumped to A/B flag changes too fast and the interviewer had to nudge me back to check the upstream funnel first.”

View Post

Atlassian·Data Scientist·Technical Phone Screen

May 2026

SQL technical screen for a Data Scientist role at Atlassian. One question, medium difficulty, e-commerce data with a couple of tables. Pretty standard stuff but the date filtering piece is where people slip up.

Given a users table and an orders table, write a SQL query that returns the top 3 users by total spend in the last 30 days. Output should include user_id, user name, and total amount spent.

“I got the join and the aggregation fine but almost forgot the date filter entirely.”

View Post

Atlassian·Data Scientist·Onsite - Behavioral / Leadership

May 2026

Behavioral loop for a Data Scientist role at Atlassian. Five questions, all structured around specific situations with measurable outcomes. The whole thing felt like they wanted real stories, not polished talking points.

Why do you want to join Atlassian specifically? Connect your motivations to the products, the mission, and how the company operates, and name two real trade-offs you're accepting by joining.
Tell me about a time you gave someone constructive feedback. How did they react, what did you do after, and what actually changed?
How do you handle competing priorities? Walk me through a recent example, the explicit trade-offs you made, and what you took away from it.
Describe a time you went above and beyond to drive a customer-facing change, but it didn't work out. What did you try, how did you measure it, and what did you learn?
What's the most effective team you've ever been part of? What made it work, what rituals or behaviors kept it working, and what did you personally bring to it?

“The trade-offs part tripped me up a bit.”

View Post

Atlassian·Data Scientist·Technical Phone Screen

May 2026

Atlassian Data Scientist interview with a coding-heavy system design problem. The question was more engineering-flavored than I expected for a DS role, which threw me off a bit.

Implement a function that takes a list of URLs (one per second) and returns '200' or '429' for each, enforcing two rate limits per address: no more than 5 requests in any 30-second window, and no more than 2 in any 5-second window.

“I knew sliding window conceptually but freezing up on the dual-window constraint cost me time.”

View Post

Atlassian·Data Scientist·Technical Phone Screen

May 2026

Atlassian data scientist interview with a pretty gnarly algorithmic problem that felt more like a competitive programming round than anything I expected for this role. The problem was well-defined but the solution space was wider than it looked at first glance.

Given an array of n integers and an integer k, place k cluster centers to minimize the maximum L1 distance from any point to its nearest center. Return that minimum possible distance.

“I sorted the array first which was the right instinct, but then I fumbled around trying to brute force placements before realizing binary search on the answer was the cleaner path.”

View Post

Atlassian·Data Scientist·Take-home Assignment

May 2026

Take-home assignment for a Data Scientist role at Atlassian. Pretty involved for a take-home, basically a full ML pipeline from data loading to model comparison to final predictions, all in one shot.

Given two CSVs (training and test sets with a binary outcome and six continuous features), write a complete ML pipeline: load the data, run diagnostics including missingness and collinearity checks, fit baseline logistic regression plus L1 and L2 regularized variants with cross-validated penalty selection, report ROC AUC and a threshold-dependent metric on the held-out set, compare models and justify your regularization choice, identify the most important features, and output final predicted probabilities with a confusion matrix.

“This took way longer than I expected.”

View Post

Atlassian·Data Scientist·Technical Phone Screen

May 2026

Atlassian Data Scientist interview with a coding question that required both a Python pandas solution and an R equivalent for labeling game outcomes. Pretty technical for a DS role but not unreasonable if you know your vectorized operations.

Given a games DataFrame with columns for team IDs and scores, write a function that categorizes each game's score margin into labels like 'Blowout Win', 'Close Win', 'Tie', 'Close Loss', or 'Blowout Loss'. Then apply it to create a new column, and provide both a vectorized pandas solution (no row-level Python loops) and an idiomatic R solution using dplyr or base R.

“The part that tripped me up initially was the 'no per-row Python loop' constraint.”

View Post