Senior Data Scientist, Algorithm, Lyft Biz

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 deci...

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 ...

Technical Leadership: Lead complex Machine Learning, AI, and causal inference initiatives across Lyft Business products (Business Travel, Lyft Pass, Concierge) in ambiguous, high-impact problem spaces.
End-to-End Modelling: Own the complete lifecycle of algorithmic solutions—from problem formulation, data exploration, and feature engineering to deployment, monitoring, and iteration.
Production Deployment: Partner closely with Engineering to build and scale production-grade ML systems, real-time inference services, batch pipelines, and feature stores.
Experimentation & Rigor: Define offline/online metrics, evaluation frameworks, and A/B testing strategies to ensure algorithms are reliable, fair, and aligned with business outcomes.
System Optimization: Continually improve model performance across latency, accuracy, cost, and reliability using advanced tuning and scientific rigor.
Algorithmic Innovation: Drive scientific excellence by introducing modern techniques in ML, optimization, reinforcement learning, or graph-based methods to unlock new product capabilities.
Cross-Functional Influence: Translate complex business challenges into concrete algorithmic solutions in close collaboration with Product, Engineering, Operations, and Science teams.
Mentorship & Quality Bar: Mentor junior and mid-level scientists, providing technical guidance, conducting modeling critiques, and contributing to Lyft's broader ML standards and tooling.
Master’s or PhD in Machine Learning, Computer Science, Statistics, Optimization, or a related quantitative field (or equivalent applied experience)
Industry Background: 5+ years of hands-on experience developing, deploying, and maintaining production machine learning models and optimization systems.
Core Technical Expertise: Deep knowledge of supervised/unsupervised learning, ranking/decisioning systems, probabilistic modeling, and causal inference.
Technical Stack: Strong proficiency in Python, modern ML frameworks (PyTorch, TensorFlow, scikit-learn), and distributed data systems (Spark, Snowflake, Databricks).
Production ML Systems: Hands-on experience building end-to-end ML architectures, including online/batch pipelines, feature engineering, and automated monitoring frameworks.
Experimental Design: Demonstrated track record of designing rigorous experimentation strategies, A/B tests, and offline/online validation methodologies.
Domain Ownership: Proven ability to independently drive multi-project algorithmic scopes and navigate technical ambiguity from ideation to delivery.Communication & Leadership: Exceptional ability to translate complex technical concepts for non-technical stakeholders, alongside a history of mentoring peers and raising technical bars.