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Website: pbsc.com
Lyft Urban Solutions, formerly PBSC Urban Solutions and originally Public Bike System Company, is an international bicycle-sharing system equipment vendor with their headquarters based in Longueuil, Quebec. The company develops bicycle-sharing systems, equipment, parts, and software, and sells its products to cities in Canada, the United States, the United Kingdom, Spain, Brazil and more. The company has sold about 280,000 bikes and 13,000 stations to 50 cities.
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Lyft is hiring a Staff Data Scientist, Decisions - Partnership, Loyalty & Pay for a full-time position in New York, NY. You will combine advanced analytics with causal inference to guide partnership, loyalty, and pay choices that keep riders and partners first. The Opportunity Data science underpins Lyft products and major decisions. As a Staff Data Scientist, Decisions - Partnership, Loyalty & Pay on the Partnership, Loyalty & Pay team inside Rider, you will identify high-value opportunities, propose technical approaches, design experiments, and quantify impact. You will work closely with product, engineering, design, research, marketing, and business development on end-to-end initiatives, and align with Rider, Marketplace, and Finance so driver, rider, and business priorities remain balanced. The environment moves quickly, values connection, and aims for a workplace where every teammate can belong and grow. Core Duties Drive the data science roadmap for Partnership, Loyalty, and Pay, helping set team goals and mark project priorities Partner with product, engineering, UX research, design, marketing, and business development to launch and scale new programs with data-centered recommendations Define and maintain metrics tied to Rider, Marketplace, and company aims, including partnership incrementality, loyalty retention effects, and Pay product health Apply modeling, advanced analytics, experimentation, and causal methods such as A/B testing, difference-in-differences, synthetic control, and quasi-experimental designs Shape strategy through data presentations for VP and C-level stakeholders and build alignment across organizations Advise PEER teams on measurement, incrementality, and causal inference as a go-to expert for PLP and dependent groups Mentor junior and mid-level scientists on design and implementation, lead code reviews, and lift technical standards Qualifications Master’s or Ph.D. in a quantitative field (statistics, economics, mathematics, computer science, or similar), or equivalent high-impact experience Six or more years in data science with causal models that delivered measurable business value Deep skill in causal inference, machine learning, experimental design, and product-focused judgment Strong command of Python and SQL for analysis and modeling Experience building measurement frameworks that use counterfactual analysis and rigorous experiments to establish true incrementality Ability to unite cross-org partners, influence technical systems, and challenge soft assumptions to steer product vision Clear…
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