Staff Data Scientist, Product & Pricing

Kindred
Remote - USPosted 11 March 2026

Job Description

Staff Data Scientist, Product & Pricing Kindred is a members-only home swapping platform that unlocks the ability to live a travel-rich lifestyle through the power of community. By exchanging primary residences with trusted peers, renters and owners alike can match with Kindred spirits and explore new destinations without breaking the bank. Kindred is revolutionizing the “third option” of travel accommodation, compared to short-term rentals and hotels. Since launching in 2022, Kindred has grown to a global community of 300,000 members across 150+ cities and puts home swapping on the map as a radically more affordable, responsible, and connected way to travel. To support that momentum, Kindred has raised over $150M in capital from some of the most credible investors in Silicon Valley, including Index Ventures, Andreessen Horowitz, New Enterprise Associates, Bessemer Venture Partners, Caffeinated Capital, Elad Gil, and founders of companies like Opendoor, Figma, ClassPass, and more. Kindred’s co-founders are proven leaders from the early team at Opendoor ($15B+ exit), who have each built and scaled products that today generate $1B+ annual revenue combined. We’re looking for the world’s top builders, executors, and believers to help us make travel more meaningful. You can learn more about us in Forbes https://www.forbes.com/sites/francescawalton/2026/02/03/home-swap-startup-kindred-raises-125-million-in-fresh-funding-as-travelers-seek-creative-ways-to-save-money/ and The New York Times https://www.nytimes.com/2025/12/16/realestate/is-your-apartment-nice-enough-for-this-home-swapping-app.html. THE ROLE: As a Staff Data Scientist, you will lead the technical strategy for our core product experience. You will move between high-level strategy, insightful data analysis and hands-on engineering to solve our most complex challenges: Matching Optimization, Growth Flywheels and Marketplace Health. You are a full-stack practitioner who can identify a friction point in the booking flow, build a predictive model to solve it, and work with engineering to ship it to production. You will define what "success" looks like for new product verticals, build the intelligent systems that power them and partner with leadership to ensure the growth of Kindred into the future. YOU WILL: - Act as a Strategic Thought Partner: Partner directly with Product Managers and other leaders to frame key business problems, define our measurement strategy, and guide product innovations. - Architect Marketplace Intelligence: Build and deploy sophisticated predictive models to refine Kindred’s matching algorithms, optimizing for growth, trust and liquidity. - Lead and Scale Experimentation: Own the end-to-end experimental framework, applying advanced causal inference to measure the ripple effects of product changes on community sentiment and marketplace health. - Own the Full-Stack Data Lifecycle: Lead the design of robust event structures with Product Engineers and collaborate with Data Engineering to build a scalable data platform that ensures our data is "born clean" and ready for modeling. - Influence and Educate: Translate complex analytical concepts into clear, actionable recommendations and effectively persuade senior stakeholders to adopt data-informed strategies. YOU MAY BE A FIT FOR THIS ROLE IF YOU: - Experience: 8+ years of experience in a quantitative role such as data science, with a proven track record of influencing strategy at a leadership level, preferably within a consumer tech or marketplace company. - Are a Full-Stack Practitioner: You are an expert in SQL and Python/R, with a proven ability to move a project from a whiteboard concept to a production-grade ML model, and experience with modern data warehouses. - Mastery of Causal Inference and Experimental Design: You have deep experience going beyond simple A/B tests to measure product impact in networked environments where interference and spillover are present. - Leadership & I ... (truncated, view full listing at source)
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