Got a DS interview coming up for a role on the insights/analytics side of the Mastercard Data and Services org. Would really appreciate recent data points from anyone who's been through the loop.
Specifically curious about: is the SQL test timed? what complexity? do they give a take-home or is it all live? what does the "case study" panel actually look like in practice how much Python/ML vs. pure analytics focus
I've heard the process can vary a lot by team so even partial info helps. drop whatever you know.
4 replies
analyst_ana
I interviewed for a Senior Analyst role (not DS) about 4 months ago. the SQL was live, timed, 45 minutes. two questions: one multi-join aggregation, one involving window functions (running totals, I think). no tricks but it moved fast. they wanted to see how you narrate while you write, not just a correct answer at the end.
de_derek
I did a loop for a data engineering adjacent role last year. the case for me was about pipeline reliability on transaction data, not really ML. so your mileage may vary by team. the "insights" org felt more SQL/BI-heavy than model-heavy from what I gathered.
ds_dmitri
this is helpful, thank you. the JD mentioned "predictive models" but I suspected it was more analytics in practice. I'll over-index on SQL and business framing and treat the ML angle as a bonus.
ml_mike
if the role touches fraud or credit risk, expect at least one question about model explainability and regulatory constraints. financial services companies care a lot about whether they can explain a model decision to a regulator. 'we used a black box and it scored well on AUC' doesn't fly.