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Lyft INC

Staff Product Manager I, Base Pricing

Toronto, Canada Posted today

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Product managementMachine learningStatisticsSQL

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No sponsorship evidence yet

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No salary bar for this route
Canada has no single salary bar: the employer usually needs a positive LMIA. Pay at or above the provincial median wage + 20% (e.g. C$36.92/h in Ontario) puts it in the high-wage stream. Source: https://www.canada.ca/en/employment-social-development/services/foreign-workers/median-wage.html, rules effective 2026-07-17.

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About the role

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. Lyft connects people to transportation to change the way we live and get around our communities. We are looking for product managers who bring exceptional creative, strategic, and analytical skills to drive forward a best in class product. Product management at Lyft leads the integration of engineering, data science, and design to achieve the company's vision for reinventing transportation. We are looking for entrepreneurial and passionate Product Managers to innovate and execute across a rapidly growing, fast paced company and industry. Pricing is the primary economic engine of Lyft's marketplace — a high-precision, real-time system that processes millions of fare calculations per hour, balancing rider affordability, driver earnings, and Lyft's sustainable growth. The Pricing team sits at the intersection of the marketplace, mediating between rider demand and driver supply, and is a core pillar of the Rider Organization. The Base Pricing Team owns the systems that set the base price of a Lyft ride for every ride type, region, and ride intent. Behind these base prices is a machine-learning and optimization stack that predicts how the Lyft marketplace responds to price, translates the cost of serving a ride into a price, and recommends price changes across hundreds of markets every week. Base prices are the foundation of every fare, and they set the reference point that dynamic pricing and rider discounts build on. The team serves two sets of customers: riders, who should see prices that are fair, competitive, and matched to what they value; and the analysts and market managers who use our models and tools to set pricing strategy. Responsibilities: As the Staff Product Manager for Base Pricing, you will own the vision and roadmap for how Lyft sets base prices, and define the success metrics that measure it. That includes: • Determining the optimal granularity at which Lyft sets base prices (e.g., by time, location, and type of trip) • Improving how our base price optimization engine works with downstream dynamic pricing systems to better respond to real-time market conditions • Re-evaluating how costs to fulfill a ride impact prices • Jointly optimizing prices across ride types, so that standard, alternative modes, and scheduled rides are priced as one portfolio • Making our impact estimation models accurate and trusted by market management teams • Evolving base pricing to function in a hybrid network with autonomous vehicles • Work closely with cross-functional stakeholders (engineers, scientists, analysts), and partner teams (Driver Earnings, Verticals, Market Management) to align on a shared vision, define requirements, and prioritize inbound requests. • Communicate roadmaps, experiment results, and pricing decisions clearly to audiences from partner teams up to Lyft's executive team. Experience: • 5+ years of product management or relevant experience, with significant time spent on algorithmic, data-driven, pricing, or marketplace products • A deep technical understanding of microeconomics, and ideally a background in Operations Research, Econometrics, Machine Learning, or algorithmic systems • Experience owning pricing, revenue management, or other economic levers with direct P&L impact; experience with price optimization, elasticity modeling, or cost modeling is a strong plus • Experience running A/B tests and market-level experiments, using statistical and causal inference techniques to evaluate results, and developing post-launch testing strategies • Experience with data, including querying, aggregation, analysis, and visualization; comfo

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