• Partner with Product, Marketing, Operations, and other teams to identify where ML can drive measurable value • Translate business problems into clear modeling objectives, metrics, and experimentation plans • Ensure ML efforts remain tightly aligned with business priorities and user impact • Lead the design, development, and iteration of ranking, filtering, and personalization models across Gametime’s product surfaces • Own modeling approaches, feature strategy, evaluation metrics, and offline and online experimentation • Balance relevance, revenue, and user trust when evolving ranking solutions • Apply LLMs and hybrid ML techniques to use cases such as semantic understanding, intent detection, content generation, and internal workflows • Evaluate emerging tools and techniques, recommending pragmatic adoption where they provide clear benefit • Establish best practices for testing, deploying, and monitoring LLM-powered models in production • Manage and mentor applied ML practitioners, supporting growth in technical depth and business impact • Set high standards for modeling rigor, experimentation discipline, and production readiness • Collaborate closely with ML engineering and platform teams to ensure scalable and reliable deployment

• Develop and implement statistical and machine learning models supporting readiness assessment, performance analysis, and injury-risk awareness. • Apply appropriate analytic methods based on data characteristics, scientific guidance, and Government priorities. • Ensure analytic approaches are transparent, interpretable, and suitable for operational decision support. • Support validation, testing, and refinement of analytic models under Government and senior scientific direction. • Assess model performance, limitations, assumptions, and potential bias. • Collaborate with epidemiologists and research psychologists to ensure analytic outputs align with scientific intent. • Work with data engineers to support feature development, data preparation, and analytic dataset construction. • Assist in identifying data quality issues that may impact model performance. • Ensure analytic inputs align with approved data definitions and governance standards. • Collaborate with software teams to support integration of analytic outputs into dashboards, reports, and user-facing decision-support tools. • Support development of repeatable analytic workflows and documentation. • Assist in troubleshooting analytic issues in coordination with data and software teams. • Contribute to documentation of analytic methods, assumptions, limitations, and results. • Support preparation of analytic summaries, briefings, and materials for Government stakeholders. • Clearly communicate analytic findings in a manner appropriate for non-technical audiences.

• You will help design and build end-to-end machine learning solutions. • You will be working in small and cross-functional product teams, with great opportunities for growth and ownership of projects. • You will be an active member of an internal community, including economists, data scientists, operations research scientists and machine learning engineers, sharing learnings, best practices and research across many domains. • You will develop high impact solutions to support Instacart's ambitious growth plans. • You will work closely with engineers, product managers, other teams, and both internal and external stakeholders, owning a large part of the process from problem understanding to recommending a solution and testing it in controlled experiments. • You will have the freedom to suggest and drive organization-wide initiatives.

• Build and maintain scalable machine learning solutions in production • Train and validate both deep learning-based and statistical-based models considering use-case, complexity, performance, and robustness • Demonstrate end-to-end understanding of applications and develop a deep understanding of the “why” behind our models & systems • Partner with product managers, tech leads, and stakeholders to analyze business problems, clarify requirements and define the scope of the systems needed • Work closely with data platform teams to build robust scalable batch and realtime data pipelines • Collaborate with software engineers, build tools to enhance productivity and to ship and maintain ML models • Drive high engineering standards on the team through mentoring and knowledge sharing • Uphold engineering best practices around code reviews, automated testing and monitoring