Applied Scientist

You'll be designing systems where the analytical choices are the product decisions. Concretely, you’ll be building pricing algorithms, designing auction mechanisms that shape how supply and demand interact, developing attendance and reliability models that determine worker↔workplace relationships, and constructing the experiment frameworks the rest of the org runs its ideas through. When a key metric moves and the cause isn't obvious, you'll run the investigation.

Methods in play include causal identification (diff-in-diff, IV, regression discontinuity), cluster-randomized trial design, discrete-time hazard modeling, mechanism design, and anomaly detection on marketplace time series. You'll work directly with engineers to take models from prototype to production, and write clearly enough to make your reasoning legible to PMs and leadership.

PhD in economics, econometrics, operations research, statistics, engineering, or a closely related field. Equivalent depth from a quant research or trading environment.

Track record of building applied models, not just publishing them; you've taken something from whiteboard to production.

Sharp experimental intuition: you know the difference between a valid identification strategy and a plausible-sounding one, and you've defended that distinction in front of a skeptical audience.

Background in quant finance, economic consulting, or marketplace work is a strong signal. We want people comfortable collaborating with competing ideas in high-stakes data environments.

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