# Data Scientist - Inference, Safety and Customer Care

**Company:** [Lyft](https://hotfix.jobs/companies/lyft)
**Location:** Toronto, Canada
**Role:** Data Science
**Salary:** CA$108k – CA$135k/yr
**Experience:** 2+ years
**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
**Posted:** 2026-08-04

> 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.

## Job Description

## 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.

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