Applied Scientist

About the role

We're building a new team, Applied Science, and we're looking for our first outside hire. 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. 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.

Responsibilities

• Build pricing algorithms
• Design auction mechanisms
• Develop attendance and reliability models
• Construct experiment frameworks
• Investigate key metrics moving without clear causes
• Apply causal identification methods (diff-in-diff, IV, regression discontinuity)
• Design and analyze cluster-randomized trials
• Use discrete-time hazard modeling
• Implement mechanism design
• Perform anomaly detection on marketplace time series

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.

Qualifications

• 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.
• 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.
• You're comfortable collaborating with competing ideas in high-stakes data environments.

Skills

• Strong analytical skills
• Experience with causal inference techniques
• Knowledge of econometric models and statistical analysis
• Ability to design and execute controlled experiments
• Proficiency in SQL or similar data querying tools

Benefits

• Competitive compensation range: $160K - $225K base + $50K - $150K equity
• Hybrid work schedule with the expectation of working at least three days per week out of our office in San Francisco

Pay

• $160K - $225K base salary

Schedule

• Hybrid work schedule with the expectation of working at least three days per week out of our office in San Francisco