The opportunity
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.
What you'll do
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.
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.
What they're looking for
- 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 innovation.
- Influence Decisions: Communicate learnings to leaders and stakeholders in a clear, compelling way that drives informed, data-driven decision-making.
- Empowerment: Think strategically about how to scale and evolve data science: capabilities within SCC, contributing to the long-term vision for how science drives platform outcomes.
