Full-Stack Data Scientist - Pricing

Bounce is building cloud storage for the physical world, starting with the largest global luggage storage network in existence. Bounce’s marketplace connects travelers with 30,000+ small business locations worldwide for hyper-local short term baggage storage. With more locations globally than Burger King, and more locations in New York City than Starbucks, Bounce has served 3M+ users and stored 10M+ bags, and paid over $10M to small business partners.

To achieve this, Bounce is a fast-paced and scrappy team. We believe that experimentation fuels innovation, so we move quickly, testing new ideas and adapting in real time. If you’re ready to make an impact in a high-energy, close-knit, and collaborative environment - Bounce is the place where you can move fast, think big, and shape the future of travel. Join us as we make the world a lighter, more accessible place! Bounce has been named the Inc5000’s fastest-growing travel company in the USA in 2024 and is proudly backed by leading Silicon Valley investors, including Andreessen Horowitz, General Catalyst, and Sapphire. (Learn more about Bounce's Series B HERE and also learn about our Japan Expansion HERE)

Decisions are at the foundation of every Bounce experience. As a Data Scientist on the Pricing team you will work in a dynamic environment, embracing moving quickly to ensure data is at the heart of how Bounce prices its marketplace — for travelers, for partners, and for the long-term health of both sides.

You will own the models behind how Bounce decides what to charge in every market, at every POI, at every store, and at every search. You'll work across Product, Supply, and Growth to manage two-sided incentives, contribution margins, and promotions, balancing short-term conversion against partner economics and lifetime value.

• Partner with cross functional teams and respective stakeholders to translate analytical insights into action

• Leverage data to track key operational metrics in dashboards, develop strategy, and forecast performance

• Help develop and maintain our analytics pipelines

• Advocate for data and data-driven decisions

• Improve our Data-Focused Agentic Layer to make operational insights self-serve for field and ops teams

• Be an owner, taking a personal stake in the success of the product and 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

• 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