Data Scientist - Inference, Safety and Customer Care
Applies causal inference, experimentation, and causal machine learning to optimize AI-powered customer support, concession allocation, retention, and operational tradeoffs. Requires at least two years of relevant industry experience plus a master's degree in a quantitative field or a relevant PhD.
About the job
Responsibilities
Inference & Measurement
- Design and implement causal inference frameworks and statistical models to measure intervention impact, evaluate system performance, and identify improvement opportunities.
Modeling
- Build, evaluate, and iterate on causal machine learning models for high-stakes decisions.
- Apply best practices across the model lifecycle, from feature engineering through production deployment.
Optimization
- Develop frameworks to analyze tradeoffs among accuracy, coverage, user experience, and operational cost.
- Propose strategies to improve overall effectiveness.
Collaboration and Influence
- Partner with Product, Design, Engineering, Operations, and Analytics.
- Communicate findings clearly to technical and non-technical leaders and stakeholders to drive data-informed decisions.
Capability Building
- Help scale and evolve data science capabilities within Safety and Customer Care.
- Contribute to the long-term vision for how data science drives platform outcomes.
Requirements
- 2+ years of industry experience in causal inference or data science with a master's degree in a quantitative field, or a PhD in a relevant field.
- Strong knowledge of causal inference and experimental design.
- Experience with uplift modeling and heterogeneous treatment effect (CATE) estimation.
- Ability to apply statistics to unstructured problems and deliver measurable results.
- Expertise in SQL and large-scale data platforms.
- Proficiency in Python and production coding environments.
- Clear and effective communication with audiences of varying technical levels.
- Strong project management, communication, and collaboration skills.
- Experience partnering with operational teams and support systems, such as customer care workflows, agent operations, or credit budget allocation.
Nice-to-Haves
- Experience with AI/LLM applications, including LLM-powered agents, retrieval systems, or evaluation frameworks.
Compensation and Benefits
- Expected base pay range in the Toronto area: CAD $108,000–$135,000.
- Extended health and dental coverage, life insurance, and disability benefits.
- Mental health benefits.
- Family building, child care, and pet benefits.
- Lyft-funded Health Care Savings Account.
- RRSP plan with company match.
- Flexible paid time off for salaried team members; hourly team members receive 15 days paid time off, with an additional day per year of service.
- 18 weeks of paid parental leave through a top-up plan.
- Subsidized commuter benefits and Lyft ride credits.
- Hybrid work schedule with at least three days per week in the office; hybrid roles may work from anywhere for up to four weeks per year.
Skills
Causal Inference, Experimental Design, Causal Machine Learning, Uplift Modeling, Heterogeneous Treatment Effects, Cate Estimation, Statistics, SQL, Python, Large-Scale Data Platforms, Feature Engineering, Production Deployment, AI Agents, Retrieval Systems, Llm Evaluation
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