Causal Data Scientist for Safety & Customer Care

Lyft; Toronto; Hybrid; CAD 108,000 - 135,000; Full time

Benefits offered by this job

Extended health & 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

Parental leave 18 weeks

Commuter benefits

Lyft ride credits

Job summary

Lyft's Safety and Customer Care (SCC) Data Science team seeks a Data Scientist specializing in causal inference to partner with engineers, product managers, and operations leaders. You will design experiments, build causal ML models, and guide data-driven decisions to improve rider and driver experiences.

You'll work on AI-powered support products, differentiated service, and operational optimization, delivering high-impact insights while navigating complex problem spaces.

Qualifications

• 2+ years of industry experience in causal inference or data science with a Master's degree in a quantitative field, or a PhD.
• Strong knowledge of causal inference and experimental design.
• Experience with uplift modeling / heterogeneous treatment effect (CATE) estimation.
• Proven ability to apply statistics to unstructured problems and deliver measurable results.
• Expertise in SQL and experience with large-scale data platforms.
• Proficiency in Python and working within production coding environments.
• Proven ability to communicate clearly and effectively to audiences of varying technical levels.
• Excellent project management, communication, and collaboration skills.
• Experience partnering with operational teams and support systems (customer care workflows, agent operations, or credit budget allocation)
• Experience working with AI/LLM applications (LLM-powered agents, retrieval systems, or evaluation frameworks) is nice to have.

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 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.

Skills

Causal inference

Experiment design

Uplift modeling

Communication

Collaboration

Education

Master's degree in statistics, economics, computer science, or equivalent

PhD in a relevant field

Tools

SQL

Python

Job description

Lyft's Safety and Customer Care (SCC) Data Science team seeks a Data Scientist specializing in causal inference to partner with engineers, product managers, and operations leaders. You will design experiments, build causal ML models, and guide data-driven decisions to improve rider and driver experiences.

You'll work on AI-powered support products, differentiated service, and operational optimization, delivering high-impact insights while navigating complex problem spaces.