Applied Scientist

About the role

We're building a new team, Applied Science, and we're looking for our first outside hire. Clipboard is a Sequoia-backed marketplace connecting nurses and healthcare professionals with long-term care facilities, with over $800M in annual transactions. You'd be joining a three-person quantitative pod with a dedicated engineering rotation that has spent the last year shipping auction systems in a live, two-sided market. We're now formalizing that work into a dedicated Applied Science function. 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. About the Work 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. Minimum 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 Who thrives here 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.