Tech Stack

Description

You will own complex incrementality measurement problems, lead causal inference initiatives, and build production-grade measurement systems at Lyft. This high-impact role shapes the future of the rideshare experience by rigorously quantifying the value of new product features.

Requirements

• Master’s or PhD in Economics, Statistics, Applied Math, Computer Science or equivalent industry experience
• 3+ years applied experience deploying causal models driving measurable outcomes
• Strong proficiency in Python and SQL
• Deep product intuition and hands-on experience with causal methods
• Ability to articulate complex causal concepts to technical and executive stakeholders

Responsibilities

• Own complex incrementality measurement problems and translate product launches into causal frameworks
• Lead high-impact Causal Inference initiatives and introduce advanced measurement techniques
• Design and deploy production-grade causal model pipelines
• Establish robust evaluation frameworks for sustainable growth
• Mentor junior/mid-level scientists and build reusable science infrastructure