Lyft · 9 hours ago
Senior Data Scientist, Algorithms - Lyft Ads
San Francisco County, CA
Full-time
Hybrid
Senior Level
$148K/yr - $185K/yr
5+ years expLyft is a company focused on building the world’s largest transportation media network. They are seeking a Senior Algorithms Data Scientist to develop algorithms for ad relevance, targeting, and optimization, working with large-scale datasets and real-time systems.
AppsMobile AppsRide SharingSoftwareTaxi ServiceTransportation
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
Qualification
Required
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, training pipelines, monitoring/alerting, and real-time inference services
Ability to define and execute offline and online evaluation strategies, including experiment design, counterfactual analysis, and diagnostics for model/system failures
Strong technical leadership skills — able to align partners, influence technical architecture, challenge assumptions, and guide cross-team modeling decisions
Demonstrated ability to mentor other scientists, elevate technical quality, and improve modeling/analysis standards across the team
Excellent communication skills, with the ability to articulate complex modeling concepts, system trade-offs, and scientific reasoning to both technical and business stakeholders
Track record of driving measurable improvements to model performance, advertiser outcomes, or system efficiency through innovative modeling or optimization techniques
Benefits
Great medical, dental, and vision insurance options with additional programs available when enrolled
Mental health benefits
Family building benefits
Child care and pet benefits
401(k) plan to help save for your future
In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
Subsidized commuter benefits
Lyft Pink - Lyft team members get an exclusive opportunity to test new benefits of our Ridership Program
Company
Lyft
Lyft is a leading ride-hailing service offering car rentals, bike, and scooter-sharing through its mobile app for convenient transportation.
5001-10000 employees
H1B Sponsorship
Lyft has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (112)
2024 (100)
2023 (132)
2022 (287)
2021 (266)
2020 (281)
Funding
Current Stage
Public CompanyTotal Funding
$5.76BKey Investors
FidelityMagna InternationalCapitalG
2025-09-03Post Ipo Debt· $450M
2024-02-21Post Ipo Debt· $400M
2021-02-01Post Ipo Equity
Leadership Team
David Risher
Chief Executive Officer
Erin Brewer
Chief Financial Officer
Recent News
2025-12-26
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2025-12-26
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