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.
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
Design, develop, and deploy production-grade algorithms and machine learning models that power Lyft Business products such as Business Travel, Lyft Pass, and Concierge.
Own the end-to-end lifecycle of modeling solutions—from problem definition, data exploration, feature engineering, and model development to validation, deployment, and post-launch monitoring.
Translate ambiguous business problems into measurable, technical solutions, collaborating closely with Product, Engineering, and cross-functional stakeholders.
Implement scalable model pipelines and contribute to high-quality, maintainable code used in production services.
Conduct deep exploratory and causal analyses on large-scale datasets to identify opportunities for growth, operational efficiency, fraud detection, dispatch quality, pricing optimization, and user experience improvements.
Run experiments and support causal inference frameworks, ensuring algorithm changes are rigorously measured and validated.
Benchmark, tune, and optimize models for performance at scale—latency, throughput, reliability, and fairness.
Contribute to technical documentation, model cards, reproducibility pipelines, and internal best practices.
Stay current with emerging ML/AI research and proactively identify opportunities to introduce new techniques that enhance product capabilities.
Collaborate in cross-functional planning, influencing direction through strong technical reasoning and data-driven insights.
Experience
Master’s degree or PhD in Machine Learning, Computer Science, Statistics, Applied Mathematics, Engineering, or a related quantitative field; or equivalent practical industry experience.
3 years of applied experience building, evaluating, and deploying ML models or algorithms in production systems.
Strong proficiency in Python, applied ML frameworks (PyTorch, TensorFlow, JAX, scikit-learn), and familiarity with ML Ops concepts (feature stores, model deployment, monitoring).
Experience working with large-scale datasets and distributed data processing tools and vendors (e.g., Spark, Snowflake, Databricks).
Hands-on experience with algorithm development, supervised/unsupervised learning, time-series, optimization, or ranking/recommendation systems.
Demonstrated ability to write clean, reliable, production-ready code and collaborate with Engineering on integrations, APIs, and model serving.
Experience with A/B testing, experiment setup, or causal inference fundamentals.
Ability to break down ambiguous problems, conduct rigorous analyses, and deliver actionable technical solutions.
Strong communication skills, with the ability to explain complex modeling concepts to technical and non-technical stakeholders.
A track record of impactful technical execution, intellectual curiosity, strong ownership, and a bias for action.
Benefits:
Extended health and dental coverage options, along with life insurance and disability benefits
Mental health benefits
Family building benefits
Child care and pet benefits