Full-Stack Data Scientist - Pricing

the team Define the base price for each market, POI, and store — using demand, competition, partner economics, and willingness-to-pay signals — and build the framework that lets us update it as conditions change Design and ship dynamic pricing logic that adjusts price by time of day, day of week, seasonality, occupancy, and local demand Build the search pricing algorithms that decide which stores to rank and at what price, balancing conversion, contribution margin, and supply fairness Manage the two-sided pricing equation — what travelers pay, what partners earn, and the take rate in between — and model the impact of changes on both sides Optimize for contribution margin, not just GMV: understand the unit economics of each reservation and design pricing that compounds margin over time Design and measure promotions, discounts, and incentives for both buyers and sellers, including launch promos, loyalty offers, and partner-side bonuses — with clean read-outs on incrementality Run causal experiments on pricing changes (geo holdouts, switchbacks, A/B where feasible) and translate the results into rollout decisions Your profile… Degree in a quantitative field such as statistics, economics, math or engineering, or relevant work experience Experience with data visualization and orchestration tools such as Amplitude, Tableau, Hex, Airflow Comfortable around data warehousing concepts, big data technologies, and analytics platforms Knowledge of Agentic Tooling: Claude, Jules or Codex are a must Strong oral and written communication skills, and ability to collaborate with and influence cross-functional partners Avid learner and practical problem solver Professional proficiency in English, written and spoken 2+ years of hands-on experience working on pricing, revenue management, or marketplace incentives at a consumer marketplace, travel/hospitality, ride-share, or e-commerce company Strong intuition for price elasticity, willingness-to-pay, and demand modeling — and the methods to estimate them (regression, ML-based demand models, structural models, or experimentation-based approaches) Experience designing dynamic pricing or ranking systems in production, ideally with feedback loops between pricing, search, and supply Comfortable with causal inference techniques appropriate for pricing — geo experiments, switchbacks, synthetic control, diff-in-diff — and aware of when simple A/B tests are not enough Can reason cleanly about two-sided marketplace incentives: how a price change affects conversion, partner earnings, take rate, and the long-term equilibrium of supply and demand Comfortable working with contribution margin and unit economics as a first-class objective, not just top-line metrics

A meeting lands on your calendar

If they’re a yes, I book the chat. You show up — that’s the whole job-hunt.