Data Scientist - Inference, Safety and Customer Care

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.

The Safety and Customer Care (SCC) team at Lyft manages over 1.7 million monthly human and AI interactions and serves as Lyft's primary direct touchpoint with riders and drivers. We handle critical infrastructure that powers both human associates and AI agents to make riders and drivers feel safe and comfortable while riding or driving with Lyft, transforming every support interaction into a moment of genuine connection.

As a Data Scientist working on Causal Inference in SCC, you'll partner with a strong team of engineers, product managers, designers, and operations leaders to deliver a personalized and exceptional experience for Lyft customers, using rigorous causal inference to guide the highest-stakes decisions we make.

We're looking for a motivated and talented Data Scientist with deep causal inference expertise to join the SCC Data Science team. You'll partner closely with the area's tech lead on high-impact work spanning AI-powered support products, differentiated service, and operations optimization.

The ideal candidate brings sharp applied inference intuition, a bias toward impact, and the ability to cut through ambiguity in complex problem spaces. You'll work on projects like:

• Design rigorous experiments and quasi-experiments to measure the causal impact of SCC product and AI-agent launches, and drive data-informed launch decisions.
• Build causal ML models to optimize concession budget allocation, targeting the right support credit, to the right rider or driver, at the right moment to maximize trust and business impact.
• Quantify the long-term effects of support-experience changes on rider and driver retention, and uncover heterogeneous treatment effects across our community.
• Deliver strategic insights on quality–cost tradeoffs, empowering leadership to balance service quality, coverage, and operational cost as we scale AI-powered support.

Responsibilities:

• Inference & Measurement: Design and implement causal inference frameworks and statistical models to measure the impact of interventions, evaluate system performance, and surface opportunities for improvement.
• Modeling: Build, evaluate, and iterate on causal ML models that power high-stakes decisions, applying best practices across the full model lifecycle, from feature engineering to production deployment.
• Optimization: Develop frameworks to analyze tradeoffs between competing objectives (accuracy, coverage, user experience, and operational cost), and propose strategies to improve overall effectiveness.
• Collaborate Cross-Functionally: Build strong relationships with partners across Product, Design, Engineering, Operations, and Analytics to drive collaboration and inno