Moloco Data Scientist Interview Experience — A SQL and Open-Ended EDA Phone Screen, Rejected Days Later

Sharing an interview report for a niche ad company.

A recruiter reached out to me on LinkedIn about an opening on a Product Analytics Data Science team. Before the interview they sent over a prep doc saying the technical phone screen would cover SQL + Python/R, with the focus on data manipulation, not modeling. The prep doc also mentioned they might ask about past project details and what I learned from them, but my actual interview didn't touch on any of that.

30 minutes of SQL:
Campaign spend table: date, advertiser_id, campaign_id, creative_group, ad_spend, impressions
Campaign conversion table: date, advertiser_id, campaign_id, creative_group, installs, kpi_cnt (different campaigns have different KPIs, so this column is the final conversion count across all of them)
Campaign goal table: campaign_id, campaign_goal, campaign_launch_date
Task: top 3 spending advertisers for each day.
Task: calculate cost_per_kpi for each goal.

15 minutes of Python:
They gave me one of Python's built-in datasets — California housing market data, including location, price, bedrooms, bathrooms, square footage, and other features for homes sold over the past 18 months — and asked what EDA I would do with it. There was no goal or background discussion at all, just "here's a dataset, what would you look at first." I was a bit thrown by this part and wasn't totally sure what they were looking for, but afterward I figured it was probably just testing basic data handling, like checking distributions and correlations. I don't know whether there would have been follow-up questions if I'd actually walked through a simple analysis.

Timeline: interviewed on a Thursday, got the rejection email the following Monday.