Applied Scientist

Clipboard•San Francisco, CA

10h•Hybrid

About The Position

Clipboard is building a new team, Applied Science, and is looking for its 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. The role involves 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. The team owns the quantitative infrastructure underneath the marketplace, including pricing algorithms, auction mechanisms, causal models, metric definitions, and experiment frameworks. Some of this work ships as product, while some becomes the analytical substrate every team in the company depends on. The work involves designing systems where analytical choices are the product decisions. This includes 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 organization runs its ideas through. The role also involves investigating key metric movements when the cause isn't obvious. 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. The individual will work directly with engineers to take models from prototype to production and write clearly to make their 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 Haves

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.
Comfortable collaborating with competing ideas in high-stakes data environments.

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 the experiment frameworks the rest of the org runs its ideas through.
Investigating key metric movements when the cause isn't obvious.
Working directly with engineers to take models from prototype to production.
Writing clearly to make reasoning legible to PMs and leadership.