> Markdown version of [/jobs/ext/3041100-data-scientist-inference-safety-and-customer-care](https://www.wearedevelopers.com/jobs/ext/3041100-data-scientist-inference-safety-and-customer-care). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - Inference, Safety and Customer Care - **Company:** Lyft Inc - **Location:** San Francisco, CA, United States (Remote available) - **Experience:** Experienced - **Salary:** $108,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Automated Storage and Retrieval Systems, Big Data, Python (Programming Language), Machine Learning, Software Deployment, SQL Databases, Feature Engineering, Large Language Models, Information Technology - **Published:** September 23, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/pm2wx4rj18 ## About the Role * 2+ years of industry experience in causal inference or data science with a Master's degree in a quantitative field (statistics, economics, computer science, etc.), or a PhD in a relevant field. * 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. ## Description The Safety and Customer Care (SCC) team at Lyft manages over 1.7 million monthly human and AI interactions and serves as Lyft's primary direct touchpoint with riders and drivers. We handle critical infrastructure that powers both human associates and AI agents to make riders and drivers feel safe and comfortable while riding or driving with Lyft, transforming every support interaction into a moment of genuine connection. As a Data Scientist working on Causal Inference in SCC, you'll partner with a strong team of engineers, product managers, designers, and operations leaders to deliver a personalized and exceptional experience for Lyft customers, using rigorous causal inference to guide the highest-stakes decisions we make. We're looking for a motivated and talented Data Scientist with deep causal inference expertise to join the SCC Data Science team. You'll partner closely with the area's tech lead on high-impact work spanning AI-powered support products, differentiated service, and operations optimization. The ideal candidate brings sharp applied inference intuition, a bias toward impact, and the ability to cut through ambiguity in complex problem spaces. You'll work on projects like: * Design rigorous experiments and quasi-experiments to measure the causal impact of SCC product and AI-agent launches, and drive data-informed launch decisions. * Build causal ML models to optimize concession budget allocation, targeting the right support credit, to the right rider or driver, at the right moment to maximize trust and business impact. * Quantify the long-term effects of support-experience changes on rider and driver retention, and uncover heterogeneous treatment effects across our community. * Deliver strategic insights on quality-cost tradeoffs, empowering leadership to balance service quality, coverage, and operational cost as we scale AI-powered support. 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. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Fault Tolerance and Consistency at Scale: Harnessing the Power of Distributed SQL Databases](https://www.wearedevelopers.com/videos/1146-fault-tolerance-and-consistency-at-scale-harnessing-the-power-of-distributed-sql-databases) - [How Machine Learning is turning the Automotive Industry upside down](https://www.wearedevelopers.com/videos/61-how-machine-learning-is-turning-the-automotive-industry-upside-down) - [The Innovation Formula: Fast Prototyping, Data Analysis, and Real User Insights](https://www.wearedevelopers.com/videos/1421-the-innovation-formula-fast-prototyping-data-analysis-and-real-user-insights) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [How software is steering vehicle technology](https://www.wearedevelopers.com/magazine/515-how-software-is-steering-vehicle-technology) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts)