Interview format
About this role
Data roles span a wide spectrum — from pure analysts who work in SQL and Excel to ML engineers who deploy production models. The interview process reflects this: most companies test a combination of SQL, statistics, product intuition (for data scientist roles), and coding (for ML engineering). Understanding which type of "data role" you're interviewing for is the first step in preparing correctly.
What to expect in a Data Scientist / Data Analyst interview
Data roles span a wide spectrum — from pure analysts who work in SQL and Excel to ML engineers who deploy production models. The interview process reflects this: most companies test a combination of SQL, statistics, product intuition (for data scientist roles), and coding (for ML engineering). Understanding which type of "data role" you're interviewing for is the first step in preparing correctly.
The distinguishing quality in strong data candidates is business orientation. Interviewers don't just want to know if you can build a model — they want to know if you can identify the right question to answer, choose the right metric, and translate the output into a decision that someone non-technical can act on. Many technically strong candidates fail data interviews because they can't explain why their analysis mattered.
For machine learning roles specifically, interviewers probe deeply on model evaluation, not just model building. They want to see you think about precision vs recall trade-offs in business terms, talk about what could go wrong in production (distribution shift, data leakage, feedback loops), and show you've seen a model fail and know why.