Clipboard · Los Angeles
Data & AIProductRemoteFullTimeposted 4mo ago
Our mission is to uplift as many communities as possible. We do this through our app-based marketplace that connects healthcare professionals with the workplaces that need amazing workers. This enables hundreds of thousands of people to achieve financial stability for themselves and their families while providing essential care to millions of people across the U.S.
Founded in 2016, we are a remote-first team of over 1,000 people building a top Y-Combinator company and have been profitable since 2022. We’re the leader in Long-Term Care staffing and are rapidly expanding into Home Health, Hospitals, and more, meaning we have more work to do than people to do it, and are growing our team to support millions more people and their communities.
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.
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.
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
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.
$160K-$225K base + $50K-$150K equity. Range reflects experience; we'll be direct about where you'd land.
This will be a hybrid role, with the expectation of working at least three days per week out of our office in San Francisco.
If this challenge seems exciting, apply!
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