Applied Scientist

We're building a new team, Applied Science, and we're looking for our first outside hire. The team owns the quantitative infrastructure underneath the marketplace: pricing algorithms, auction mechanisms, causal models, metric definitions, and experiment frameworks. Some of that work ships as product; some becomes the analytical substrate every team in the company depends on. 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.

Responsibilities

Designing systems where the analytical choices are the product decisions; Building pricing algorithms; Designing auction mechanisms that shape how supply and demand interact; Developing attendance and reliability models that determine worker↔workplace relationships; Constructing experiment frameworks the rest of the org runs its ideas through; Running investigation when a key metric moves and the cause isn't obvious; Working directly with engineers to take models from prototype to production; Writing clearly enough to make your reasoning legible to PMs and leadership.

Requirements

Bachelor’s degree in quantitative field: economics, statistics, engineering, mathematics, etc or commensurate practical experience.; Experience building and deploying quantitative models (in applied or research settings); Comfort querying data directly (SQL or equivalent); Experience designing and analyzing controlled experiments

Nice to have

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.

Benefits

Equity ($50K-$150K)