Senior Data Scientist, Lyft
Algorithms, Lyft Business
CA$136-170k
More information about location
Ride sharing company
Open for applications
Ride sharing company
1001+ employees
Open for applications
CA$136-170k
More information about location
1001+ employees
Company mission
To improve people’s lives with the world’s best transportation.
Role
Who you are
- The ideal candidate will bring deep expertise in algorithm development, machine learning, causal inference, experimentation; strong business acumen in B2B contexts; and a proven track record of leading teams in fast-paced, cross-functional environments
- Master’s or PhD in Machine Learning, Computer Science, Optimization, Statistics, Engineering, or a related quantitative discipline; or equivalent strong applied experience
- 5+ years of industry experience developing and deploying machine learning models, algorithms, or optimization systems in production environments
- Demonstrated ability to independently own multi-project, multi-component algorithmic scopes, especially in ambiguous or highly technical problem domains
- Deep expertise in: Supervised and unsupervised learning, Optimization, probabilistic modeling, or time-series modeling, Ranking systems, decisioning, or reinforcement learning, Feature engineering, pipeline architecture, and ML deployment
- Strong proficiency in Python, ML frameworks (PyTorch, TensorFlow, JAX, scikit-learn), and distributed data processing (Spark, Snowflake, Databricks)
- Hands-on experience building production-grade ML systems, including real-time inference services, batch pipelines, and monitoring/alerting frameworks
- Demonstrated ability to define experiment design, offline metrics, online metrics, and success criteria for model evaluation
- Strong communication and influence skills — capable of explaining technical tradeoffs, model behaviors, and system constraints to both technical and non-technical partners
- Experience mentoring others, raising modeling standards, and contributing to internal ML best practices
- Proven track record of delivering high-quality, well-integrated algorithmic solutions that drive measurable product and business outcomes
What the job involves
- Data Science is at the heart of Lyft’s products and decision-making
- Data Scientists 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 Business builds products that help organizations move the people who matter most—employees, customers, patients, and guests—easily and efficiently
- Our offerings include Business Travel, Lyft Pass, and Concierge (for healthcare and non-healthcare rides), enabling companies to manage transportation at scale through APIs, integrations (e.g., Concur, Expensify), and dedicated tools
- These platforms power high-impact B2B use cases across corporate travel, healthcare access, customer experience, and community programs
- We are seeking a Data Scientist to lead initiatives across the entire Lyft Business product suite
- In this role, you will shape the vision, define the roadmap, and drive execution for data science projects that accelerate growth, improve operational efficiency, and deliver measurable value to our partners
- You’ll collaborate closely with Product, Engineering, Design and Go-to-Market teams to build models, experimentation frameworks, and advanced analytics that inform strategy and power product innovation
- This is a high-visibility, high-impact role with direct influence on Lyft’s enterprise offerings
- Lead multiple Machine Learning and AI initiatives across Lyft Business (Business Travel, Lyft Pass, Concierge), operating in open-ended and ambiguous spaces with multi-team impact
- Design, develop, and deploy advanced ML models, optimization algorithms, and ranking/decision systems that power core product and platform capabilities
- Own the end-to-end lifecycle of complex modeling solutions — problem formulation, data exploration, model development, evaluation, deployment, and ongoing iteration
- Partner closely with Engineering to build scalable, production-grade systems, including online inference services, batch pipelines, feature stores, monitoring, and model governance
- Define model evaluation strategies, experimentation plans, and offline/online validation methods, ensuring algorithms are robust, reliable, fair, and aligned with business outcomes
- Improve model performance across latency, accuracy, stability, cost, and system reliability, employing advanced tuning, optimization, and scientific rigor
- Build and maintain high-quality codebases for models, training pipelines, diagnostics, and simulation frameworks; enforce best practices around reproducibility and documentation
- Drive algorithmic innovation, introducing new techniques in ML, optimization, reinforcement learning, causal inference, or graph-based methods that unlock new product capabilities
- Collaborate across Product, Engineering, Ops, and Science to translate ambiguous business problems into algorithmic solutions with measurable success criteria
- Mentor junior and mid-level applied scientists and data scientists, providing technical guidance, code reviews, modeling critiques, and helping raise the scientific bar
- Contribute to Lyft’s broader Science community, particularly around ML tooling, modeling standards, experimentation practices, and reusable algorithmic frameworks
Our take
Lyft has aimed for consistent growth, making sure to pitch itself as a more reliable and friendly ride-sharing option than its main competitor, Uber. Uber has a greater market share and operates globally, but Lyft hopes to compete by its focus on ride-sharing over Uber's more diversified business approach and as a more rider-friendly and greener alternative to Uber.
Operating only in the USA and Canada, Lyft is in a position of being less affected by global events than Uber but also more vulnerable to local conditions. As an example, while it doesn't face the regulatory hurdles around employment rights that Uber does, it is unable to balance out the cost of the unusually expensive US auto insurance across its operations.
In 2022, Lyft acquired PBSC Urban Solutions, a bike-share equipment and technology supplier, allowing Lyft to compete in new verticals and reaffirming its commitment to green transportation. It has also trialled and launched Lyft Assisted, where drivers help passengers from their door into the car to get to medical appointments. These developments chime well with Lyft's unique selling point as a friendlier and more environmentally aware ride-sharing app, whilst the acquisition of European competitor Freenow could add 6 million new riders to its platform in 2026.
Steph
Company Specialist at Welcome to the Jungle
Insights
-2% employee growth in 12 months
Company
Funding (last 2 of 14 rounds)
Jun 2018
$600m
LATE VC
Mar 2018
$200m
LATE VC
Total funding: $4.9bn
Company benefits
- Great medical, dental, and vision insurance options
- Mental health benefits
- In addition to 12 observed holidays, salaried team members have unlimited paid time off, hourly team members have 15 days paid time off
- 401(k) plan to help save for your future
- 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
- Pre-tax commuter benefits
- Lyft Pink - Lyft team members get an exclusive opportunity to test new benefits of our Ridership Program
- Family building benefits
- Lyft Pink - Lyft team members get an exclusive opportunity to test new benefits of our Ridership Program
Company HQ
China Basin, San Francisco, CA
Leadership
John Zimmer
(Co-Founder)Originally an Analyst at Lehman Brothers, Zimmer then founded Zimride which was a private ridesharing company that pivoted into Lyft.
Logan Green
(Co-Founder)Founded Zimride, a private ridesharing company which later pivoted into Lyft.
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