Causal Data Scientist, Safety & Customer Care

Lyft; Toronto (OH); Hybrid; USD 78,000 - 97,000; Full time

Benefits offered by this job

Extended health and dental coverage

Mental health benefits

Family building benefits

Child care and pet benefits

Lyft Health Care Savings Account

RRSP plan with company match

Flexible paid time off

Paid parental leave (18 weeks)

Subsidized commuter benefits

Lyft ride credits

Job summary

Lyft is seeking a Data Scientist in the Safety and Customer Care (SCC) team to apply causal inference to optimize rider and driver experiences. You will design experiments, build causal ML models, and quantify long-term effects of support-experience changes while working with product, engineering and operations leaders.

The role emphasizes rigorous inference, production-ready modeling, and data-driven decision making.

Qualifications

• 2+ years of industry experience in causal inference or data science with a quantitative degree.
• Strong knowledge of causal inference and experimental design.
• Experience with uplift modeling / heterogeneous treatment effects (CATE).

Responsibilities

• Design and implement causal inference frameworks and models to measure interventions and surface opportunities for improvement.
• Build causal ML models powering high-stakes decisions across SCC products and AI agents.
• Analyze tradeoffs between accuracy, coverage, user experience and cost to inform strategy.
• Collaborate with Product, Design, Engineering, Operations, and Analytics to drive cross-functional results.
• Communicate findings clearly to stakeholders to influence decisions.

Skills

Causal inference

Experiment design

Uplift modeling

SQL

Python

Communication

Project management

Cross-functional collaboration

AI/LLM experience

Education

Master's in quantitative field

Tools

Production coding environments

Job description

Lyft is seeking a Data Scientist in the Safety and Customer Care (SCC) team to apply causal inference to optimize rider and driver experiences. You will design experiments, build causal ML models, and quantify long-term effects of support-experience changes while working with product, engineering and operations leaders.

The role emphasizes rigorous inference, production-ready modeling, and data-driven decision making.