Senior Data Scientist, Algorithm, Lyft Biz

About the role

Lyft Business is seeking a Senior Data Scientist to own the technical strategy and execution for our enterprise product suite. In this role, you will define the algorithmic roadmap for high-impact B2B products such as Business Travel, Lyft Pass, and Concierge. You will lead complex initiatives involving Machine Learning, AI, and causal inference to solve ambiguous and critical business problems. The position requires close collaboration with cross-functional partners to translate business needs into production-grade data products. You will be responsible for driving scientific excellence and ensuring that models deliver measurable value at scale. This is a high-visibility role with direct influence on Lyft's enterprise growth and operational efficiency. The ideal candidate will mentor other scientists and establish rigorous standards for algorithm development and deployment. This role is based in San Francisco, CA and is a full-time position.

What you'll do

Lead technical direction for Machine Learning, AI, and causal inference initiatives across Lyft Business products including Business Travel, Lyft Pass, and Concierge. Own the complete lifecycle of algorithmic solutions, covering problem formulation, data exploration, feature engineering, deployment, monitoring, and iterative improvement. Partner with Engineering teams to build and scale production-grade ML systems, real-time inference services, batch pipelines, and feature stores. Define offline and online metrics, evaluation frameworks, and A/B testing strategies to ensure algorithms are reliable, fair, and aligned with business outcomes. Continuously improve model performance across dimensions such as latency, accuracy, cost, and reliability using advanced tuning and scientific rigor. Drive innovation by applying modern techniques in machine learning, optimization, reinforcement learning, or graph-based methods to unlock new product capabilities. Translate complex business challenges into concrete algorithmic solutions through close collaboration with Product, Engineering, Operations, and Science teams. Mentor junior and mid-level scientists, providing technical guidance, conducting modeling critiques, and contributing to Lyft's broader ML standards and tooling. Develop experimentation frameworks that enable data-driven decision-making and validate the impact of algorithmic changes. Collaborate with stakeholders to identify opportunities where data science can enhance operational efficiency and customer experience. Design and implement experiments to test hypotheses and measure the true impact of proposed algorithmic improvements. Ensure that all solutions adhere to best practices for scalability, maintainability, and robustness in production environments. Work with data engineers to improve data quality, feature availability, and pipeline reliability to support advanced modeling efforts. Contribute to the broader data science community within Lyft by sharing insights, methodologies, and tools.

Requirements

Master's or PhD in Machine Learning, Computer Science, Statistics, Optimization, or a related quantitative field, or equivalent applied experience. Industry background with 5+ years of hands-on experience developing, deploying, and maintaining production machine learning models and optimization systems. Deep knowledge of supervised and unsupervised learning, ranking and decisioning systems, probabilistic modeling, and causal inference. Strong proficiency in Python and modern ML frameworks such as PyTorch, TensorFlow, and scikit-learn. Experience with distributed data systems including Spark, Snowflake, and Databricks. Hands-on experience building end-to-end ML architectures, including data pipelines, feature stores, model training, and deployment. Demonstrated ability to design and execute A/B tests and analyze experimental results to inform product decisions. Excellent problem-solving skills and the ability to work effectively in ambiguous, fast-paced, and cross-functional environments.

Nice to have

Lyft values scientific excellence and may prefer candidates who have demonstrated experience with modern ML techniques, optimization methods, and production-scale system design. Preferred backgrounds include work in transportation, logistics, or other dynamic operational domains where data-driven decisions directly impact user experience and business outcomes. Candidates who have contributed to open source projects, published research, or internal tooling that showcases algorithmic innovation and rigorous experimentation will be viewed favorably. Experience mentoring junior scientists or leading cross-functional technical initiatives is considered a plus.