Data Science Manager, Machine Learning - Lyft Ads

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. ML & Data Science is at the heart of Lyft's products and decision-making. ML and Data Science professionals at Lyft operate in dynamic environments, moving quickly to build the world's best transportation solutions. We tackle a wide range of challenges—from shaping long-term business strategy with data, to making critical short-term decisions, to developing algorithms and models that power both internal systems and customer-facing products. Lyft Ads is Lyft's advertising platform, connecting brands with high-intent audiences across the rideshare journey. Our offerings span in-app ad formats, in-car tablet experiences, bikeshare and station sponsorships, and programmatic integrations—enabling advertisers to reach riders at key moments of engagement. These products power high-impact use cases across brand awareness, performance marketing, audience targeting, and measurement for enterprise advertisers. We are seeking an Algorithms Science Manager to lead a team of Data Scientists, Applied Scientists, and Machine Learning Engineers building the algorithmic backbone of Lyft Media. In this role, you will shape the vision, define the roadmap, and drive execution for projects that improve ad relevance, optimize yield, enhance targeting and measurement, and deliver measurable value to our advertising partners. You'll collaborate closely with Product, Engineering, Design, and Sales teams to build models, experimentation frameworks, and production ML systems that inform strategy and power product innovation. This is a high-visibility, high-impact role with direct influence on Lyft's advertising platform and revenue growth. The ideal candidate will bring deep expertise in algorithm development, machine learning, causal inference, and experimentation; strong business acumen in ads or marketplace contexts; and a proven track record of leading multi-disciplinary technical teams in fast-paced, cross-functional environments. Responsibilities: Lead, mentor, and grow a high-performing, multi-disciplinary team spanning Applied Science. Data Science, and Machine Learning Engineering for Lyft Media. Define and execute the technical vision and roadmap for the team, ensuring alignment with overall business strategy and revenue goals across research, modeling, and production ML. Design, develop, and deploy algorithms and ML systems that power core advertising capabilities—including ad targeting, audience segmentation, bid optimization, attribution, and yield management. Partner with Product, Engineering, and Design to integrate solutions into scalable, production-grade ad serving and measurement systems. Establish robust experimentation and causal inference frameworks to measure the impact of algorithmic changes on advertiser outcomes, rider experience, and platform revenue. Bridge the gap between research and production—ensuring that applied science innovations translate into reliable, maintainable ML systems at scale. Conduct deep analyses of complex, large-scale datasets to uncover opportunities for revenue growth, advertiser performance improvement, and enhanced user experience. Champion data-driven decision-making, ensuring that product and go-to-market decisions are informed by rigorous quantitative analysis. Drive innovation by staying current with emerging research, technologies, and industry best practices in computational advertising, optimization, and applied machine learning. Experience: PhD (preferred) or Master's degree in a quantitative field such as Machine Learning, Computer Science, Statistics, Engineering, or a related discipline; or equivalent practical experience. 8+ years of progressive experience in machine learning, optimization, or causal inference, including building and deploying algorithms in production systems. 3+ years of people management experience leading multi-disciplinary technical teams (data science, applied science, and/or ML engineering), with a proven ability to mentor, develop, and retain top talent. Demonstrated ability to set a strategic vision for a technical team and translate it into impactful, scalable solutions that drive measurable business outcomes. Deep expertise in machine learning, experimental design, causal inference, and statistical methodologies, with a track record of applying them to high-stakes product or marketplace decisions. Strong understanding of ML engineering best practices—model training infrastructure, feature pipelines, model serving, and monitoring in production environments. Experience in advertising technology, media measurement, or marketplace optimization is strongly preferred. Experience navigating complex, ambiguous problem spaces and guiding teams through prioritization, tradeoffs, and execution. Strong communication and influence skills, with the