have a Paramount data science screen in a few weeks (content analytics team) and trying to get a read on what their loop actually looks like in 2025-2026. the info on glass door is pretty stale.
specifically trying to understand: do they use take-homes, do they do SQL-heavy rounds, is there a case study component, and how much does the panel care about streaming metrics vs general DS skills. anyone been through a data or analytics loop recently, please drop what you remember. timelines and round structure especially helpful.
4 replies
analyst_ana
went through a data analyst process there (not data science, but adjacent) about 8 months ago. they gave a take-home with a dataset around viewer engagement. the questions were practical: retention curves, cohort analysis, a short slide deck presenting findings. not super hard technically but they wanted you to frame insights in terms of content decisions, not just stats.
de_derek
i interviewed for a data engineering role about a year ago. SQL round was definitely present, not brutal but they expected window functions and some aggregation logic. the system design piece was about pipeline architecture for event streaming from their apps. they're on AWS, snowflake for the warehouse side from what i can tell. the panel was two data engineers and a manager, all pretty chill.
ds_dmitri
super helpful, thank you. snowflake confirms what a friend mentioned. good to know the sql isn't at the "write a database engine from scratch" tier.
corp_refugee
coming from a big tech background the analytics rigor there will feel like a step down. which isn't a criticism, it's just a different context. they care a lot more about whether you can translate numbers to content strategy stakeholders than whether you can implement a gradient boosting model from scratch.