Senior Data Scientist - Marketing (Hybrid)
Posted Sep 11, 2026 · We last checked this listing on Oct 6, 2026
Likely interview questions for this role
Written from this job description, not a generic list. Each one notes what the interviewer is really checking.
About your past
Tell me about a time your value or LTV model disagreed with what another team's model said the customer was worth. How did you resolve it?
What they are checking: ability to reconcile competing analytical frameworks and collaborate rather than just defend their own model
Tell me about a time you had to explain a modeling decision to people who weren't technical, and it actually changed what they decided to do.
What they are checking: communication skill and whether their analysis actually drove business action, not just got shared
Describe a situation where you had to work independently on an ambiguous problem with no clear playbook. How did you decide where to start?
What they are checking: self-direction and structured problem-solving in a fast-paced, loosely defined environment
Tell me about a model you built that got deployed and then had to be maintained over time. What broke, and how did you find out?
What they are checking: experience with the full model lifecycle including monitoring and maintenance, not just building and handing off
What would you do
Suppose the Pricing and Profitability team updates their core LTV model and the assumptions shift. Your bidding models are already live and spending real budget. What do you do first?
What they are checking: judgment about production risk, versioning, and how they'd manage dependency on another team's evolving model
If Meta's bidding algorithm started spending disproportionately on leads with high predicted value but the actual downstream conversion or repayment didn't match, how would you investigate whether the problem is your model, their platform, or something else?
What they are checking: diagnostic thinking and comfort separating model error from platform behavior and external noise
If leadership asked you to justify shifting spend away from a channel that looks efficient on last-click conversions but you suspect is bringing in low lifetime value customers, how would you make that case with data?
What they are checking: ability to translate the core value-based bidding argument into a persuasive, evidence-backed business recommendation
About the work
Walk me through how you would build a model that predicts customer lifetime value at the moment someone just clicks an ad, before we know almost anything about them.
What they are checking: whether the candidate understands early-funnel modeling with sparse features and can reason about signal availability at different funnel stages
Say you've got a value prediction ready to send to Google Ads through an offline conversion upload. Tell me how that pipeline actually works end to end and what could go wrong with latency or data quality along the way.
What they are checking: hands-on knowledge of conversion APIs and offline upload mechanics, and whether they think operationally about reliability, not just modeling
How would you design an experiment to prove that value-based bidding actually improved acquisition efficiency, not just shifted which customers we happened to acquire that month?
What they are checking: depth in causal inference and incrementality testing versus just correlational before/after comparisons
How do you think about the tradeoff between a model that's more accurate but slower to update, versus one that's simpler and can react to fresh data faster, when it's directly driving ad spend?
What they are checking: understanding of the real-time constraints unique to feeding bidding algorithms versus offline analytics
What's your experience with SQL and Python for pulling together marketing and financial data that lives in different systems, and can you describe a messy data integration you've had to solve?
What they are checking: concrete technical fluency and comfort with real-world data plumbing, not just clean datasets
How would you approach allocating budget across paid search, paid social, and other channels when each platform reports success differently and attribution isn't perfectly clean?
What they are checking: grasp of cross-channel measurement challenges and skepticism toward platform-reported numbers
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As published by Enova.