Senior Data Scientist, Algorithms - Lyft Business

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.

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. 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.

Responsibilities:

• 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.

Experience:

• 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 le