Senior Manager, Data Science

** CULTIVATE A BETTER WORLD
** Food served fast does not have to be a typical fast-food experience. Chipotle has always done things differently, both in and out of our restaurants. We are changing the face of fast food, starting conversations, and directly supporting efforts to shift the future of farming and food. We hope you will join us as we continue to learn, evolve, and shape what comes next on our mission to make better food accessible to everyone.
** THE OPPORTUNITY
** Chipotle is building a modern, enterprise-grade Data Science organization focused on reliability, governance, and the delivery of high-impact machine learning solutions. As Senior Manager, Data Science, you will lead a team of data scientists and/or analytics engineers responsible for developing, deploying, and operationalizing production ML models that improve decision-making across Digital, Restaurant Operations, Supply Chain, Finance, Marketing, and the broader enterprise.

You will partner closely across Data Engineering, BI Engineering, Product & Technology, and business teams to define model development standards, experimentation frameworks, and operational practices that ensure long-term model reliability and transparency. This role will also play a key part in supporting Chipotle’s Data Science and AI vision and roadmap by helping evaluate emerging capabilities, aligning ML efforts with platform strategy, and advancing responsible, strategic adoption of AI technologies.

This is a hands-on leadership role that translates data science strategy into reliable, scalable solutions that power the business every day.
** LOCATION
* * This position will be based in our Newport Beach, CA office 4 days per week (with work from home on Friday). Remote work is not available for this role.
** WHAT YOU’LL DO
*** Leadership & Strategy:  + Lead, mentor, and grow a high-performing Data Science team of data scientists and/or analytics engineers.  + Partner with the Director of Analytics Engineering to build and execute Chipotle’s Data Science strategy, roadmap, operating model, and AI enablement goals.  + Support the development and adoption of frameworks, guidelines, and capabilities that enhance Chipotle’s readiness for scalable AI integration.  

+ Define and enforce modern data science, analytics engineering, and MLOps best practices.  + Establish standardized frameworks for experimentation, causal inference, model validation, and success measurement.  + Promote a culture of documentation, peer review, testing, and operational excellence.
* Data Science, MLOps & AI Execution:  + Own the complete ML lifecycle—from problem framing through data sourcing, modeling, deployment, monitoring, and continuous improvement.  + Build, manage, and maintain reliable feature pipelines and ML workflows.  + Deploy production ML models and implement observability for drift, data quality, concept changes, bias, performance, and SLAs.  + Ensure reproducibility through versioning, testing, documentation, and proper model lineage.  

+ Modernize Chipotle’s data science capabilities by exploring and evaluating emerging technologies and future platform capabilities, including potential expansion into cloud-native ML platforms.  + Support Chipotle’s AI enablement efforts by helping evaluate emerging AI capabilities and potential applications.  + Ensure all ML products follow governance, risk, security, and explainability expectations.

* Cross-Functional Collaboration:

+ Partner with Digital, Marketing, Restaurant Operations, Finance, Supply Chain, Product, and other business teams to identify and prioritize high-value ML opportunities.  + Collaborate with engineering teams to design production-ready ML solutions based on enterprise platform strategy.  + Partner with the Data Engineering team to strengthen Data Science–owned transformations and ensure high-quality, reliable data pipelines.  + Contribute to platform modernization efforts by helping define when Data Science should use specific data transformation patterns or workflows in alignment with the broader platform strategy.  

+ Translate business needs into statistical, machine learning, causal inference, or…