Canva Data Scientist candidates report an early fit conversation, hands-on SQL work, and later technical discussions spanning statistics, modeling, clustering, and ML system design.
$148K
Avg. Base Comp
$180K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Reported Canva Data Scientist interviews begin with an HR or phone conversation focused on the resume, role fit, values, and motivation for Canva. One candidate also learned about the Data Science team structure and project types during that discussion.
Expect to write SQL and defend your choices. Candidates describe practical work with joins, aggregations, GROUP BY, HAVING, NULL values, CTEs, window functions, and metrics across multiple tables. A reported prompt asked for each user’s design count while also identifying users who had created no designs. Follow-up questions explored the selected approach, edge cases, and the complexity of joins or functions. Practice narrating query logic clearly, especially when a left join or conditional aggregation is needed to retain inactive users.
The technical material reported across the accounts also includes A/B experiments, event analytics, metric definition, statistics, math, coding, Python, clustering from scratch with an evaluation script, and ML system design for a classification problem with human intervention. One account described a HackerRank assessment and a longer technical session; another reported the HR and SQL stages. Prepare broadly, but prioritize execution: solve the SQL problem, state assumptions for a metric or experiment, and explain modeling or human-review trade-offs in plain language.
Synthesized from 2 candidate reports by our editorial team.
Had an interview recently?
Share your experience. Unlock the full guide.
Real interview reports from people who went through the Canva process.
I had a first-round interview with HR, where we discussed my resume, the role, and how my experience aligned with the position. The HR representative also explained the Data Science team structure and the types of projects they work on.
The next stage was a SQL-focused technical interview. I was given practical SQL questions involving joins, aggregations, GROUP BY, HAVING, handling NULL values, and calculating metrics from multiple tables. I had to explain my approach and write queries to solve the problems rather than simply describe SQL concepts. The interviewer also asked follow-up questions to test my understanding of why I chose a particular approach and how I would handle different edge cases.
Questions included SQL, CTEs, window functions, joins, A/B experiments, event analytics, and metric definition. One prompt asked me to write a SQL query returning every user with the number of designs they created, while identifying users who had never created a design.
Prep tip from this candidate
Practice writing SQL that preserves users with no matching records, then explain your join choice, edge cases, and metric logic. Review A/B experiment basics, event analytics, and metric definitions.
Share your own interview experience to unlock all reports, or subscribe for full access.
Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Canva
Write a query that returns all neighborhoods that have 0 users.
| Question | |
|---|---|
| Search Ranking | |
| Generate Shopping List from Recipes | |
| Hurdles In Data Projects | |
| Duplicate Rows | |
| Digital Marketing Metrics | |
| International e-Commerce Warehouse | |
| Electricity Supply | |
| k-Means from Scratch | |
| Statistically Significant Test | |
| 2nd Highest Salary | |
| Merge Sorted Lists | |
| Top Three Salaries | |
| Rolling Bank Transactions | |
| Customer Orders | |
| Comments Histogram | |
| First to Six | |
| Closest SAT Scores | |
| Subscription Overlap | |
| Experiment Validity | |
| Download Facts | |
| Monthly Customer Report | |
| Upsell Transactions | |
| First Touch Attribution | |
| Random SQL Sample | |
| Button AB Test | |
| Compute Deviation | |
| Top 3 Users | |
| Find the First Non-Repeating Character in a String | |
| String Shift |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an early HR or phone conversation about their resume, alignment with the role, values, and motivation for Canva. One candidate also discussed the Data Science team structure and project types. Prepare a concise account of relevant work and why it fits the role.
Candidates report a SQL-focused interview requiring written queries rather than definitions. Topics included joins, aggregations, `GROUP BY`, `HAVING`, NULL values, CTEs, window functions, and metrics from multiple tables. Follow-ups covered the chosen approach and edge cases; one prompt involved design counts and users with no designs.
One candidate reports assessment and technical material covering coding, statistics, math, Python, AI, modeling, clustering from scratch with an evaluation script, behavioral questions, and ML system design for a classification problem with human intervention. A/B experiments, event analytics, and metric definition were also reported.
