- Exceptional experimental design skills. You've designed and run many experiments, in the field and in live commercial settings, and know how to get clean answers from messy real-world conditions.
- Deep expertise in causal inference, including randomized experiments, quasi-experimental methods and incrementality measurement.
- Hands-on experience with advertising and campaign data: impressions, CTR, conversions, CPA, ROAS, attribution and lift measurement.
- A working understanding of how ad platforms and delivery algorithms behave (auctions, optimization, pacing, targeting) and how they bias experimental results.
- Commercial experience in ad tech, marketing measurement, media, consumer intelligence, market research or consumer-facing products.
- Strong experience validating predictive or modeled data: calibration, generalization, uncertainty and bias.
- Advanced quantitative training (PhD or equivalent experience) in a field such as social or quantitative psychology, computational social science, statistics, economics, quantitative marketing or a related discipline.
- An instinct for the difference between a model that predicts well and one whose outputs mean what they claim to.
- Experience with geographic or multilevel data is a plus.
- Strong coding in Python and/or R, with comfort working directly with large datasets.
- Fluency with Claude Code, including an understanding of AI failure modes and how to verify output.
- The ability to explain complex methods simply and persuasively to commercial audiences.
Backgrounds that could be a strong fit
We care more about experimental rigor, commercial judgment and creativity than about a specific discipline. Strong candidates might come from marketing science, ad measurement, experimentation teams at ad platforms or tech companies, media agencies, behavioral science, quantitative social science, economics or market research.