Job Specifications
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 Ads is one of Lyft’s newest and fastest-growing businesses, focused on building the world’s largest transportation media network. Our mission is to help brands reach riders during key moments of their journey—before, during, and after a ride—by delivering meaningful, contextually relevant ad experiences. We operate at the intersection of mobility data, real-time decision systems, and AI-powered personalization, enabling advertisers to run high-impact campaigns with measurable outcomes.
We are seeking a Senior Algorithms Data Scientist to help build the next generation of ads relevance, targeting, optimization, and measurement algorithms that power the Lyft Ads platform. In this role, you will work across large-scale datasets and complex real-time systems to design, prototype, and deploy production-grade machine learning models. You’ll collaborate closely with Engineering, Product, Data Science, and Sxales to translate ambiguous business and advertiser needs into rigorous algorithmic solutions that improve ad performance, enhance marketplace efficiency, and drive meaningful revenue growth.
This is a high-impact, highly technical role within a rapidly scaling business line. The ideal candidate brings strong applied machine learning intuition, hands-on modeling experience, and the ability to write clean, efficient production code. You will play a critical role in shaping how advertisers connect with Lyft riders—pushing the boundaries of personalization, measurement, and real-time optimization in a dynamic marketplace.
Responsibilities:
Lead multiple high-impact Machine Learning and AI initiatives across the Lyft Ads platform — including relevance, targeting, bidding, pacing, delivery optimization, conversion prediction, and measurement systems.
Define the modeling strategy, technical roadmap, and success metrics for ML components that power ad-serving and advertiser performance, ensuring alignment with business and revenue goals.
Own complex, open-ended problem spaces, breaking down ambiguous advertiser, marketplace, and system constraints into well-structured modeling approaches and scientific requirements.
Design, develop, and deploy advanced machine learning, optimization, and decisioning algorithms for large-scale real-time and batch systems, balancing scientific rigor with practical engineering constraints (latency, throughput, cost, reliability).
Partner deeply with Ads Engineering, Infra, and Product to architect production-grade ML systems — including feature stores, training pipelines, online scoring services, monitoring, A/B frameworks, and model governance processes.
Establish robust evaluation frameworks, defining offline metrics, calibration checks, counterfactual methods, experiment designs, and long-term measurement strategies to ensure model correctness and system stability.
Diagnose systemic issues (drift, feedback loops, cold start, pacing imbalance, auction inefficiencies) and lead cross-functional efforts to improve model performance, user experience, and advertiser ROI.
Drive algorithmic innovation by introducing new techniques from ranking, causal inference, reinforcement learning, probabilistic modeling, graph ML, or optimization, and evaluating their feasibility for large-scale ads systems.
Build reusable modeling infrastructure, libraries, and best practices, enabling faster iteration and higher modeling quality across the broader Ads Science and Engineering teams.
Mentor and guide junior/mid-level scientists and MLEs, serving as a technical advisor on modeling design, experimentation, code quality, and scientific reasoning.
Represent Algorithm Science in cross-functional planning, ensuring algorithms are grounded in strong methodology and aligned with Ads business priorities, advertiser needs, and platform constraints.
Experience:
Master’s or PhD in Machine Learning, Computer Science, Optimization, Statistics, Engineering, Applied Mathematics, or a related quantitative field; or equivalent high-impact industry experience.
5+ years of applied science or machine learning experience, with a track record of deploying production models that drive measurable business outcomes.
Demonstrated ability to own multi-project modeling scope across ambiguous problem spaces and integrate work across engineering, product, and data science partners.
Deep expertise in:
Ranking and relevance modeling
CTR/CVR prediction, calibration, and uncertainty modeling
Optimization and pacing algorithms
Auction dynamics or marketplace delivery systems
Causal inference methods for ads measurement
Strong proficiency in Python, ML frameworks (PyTorch, TensorFlow, JAX, scikit-learn), and distributed data systems (Spark, Snowflake, Databricks).
Proven experience building large-scale, production-ready ML systems, including model servers, traini