Senior Manager, Operations Research and Analytics Development

Working with Us
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Position Summary

The Analytics Development team, part of Product Development & Supply Business Insights & Technology (PDS BI&T), partners with the broader PDS organization to answer key business questions that drive business strategy using advanced data analytics and data science.

The Senior Manager, Operations Research and Analytics Development will partner with SMEs across the Analytics Development organization to design, build, and scale core tools that serve as the technical foundation for analytics solutions. These solutions will support multiple PDS verticals, including Product Development (PD), Global Business Unit (GBU), Global Supply Chain (GSC), Global Quality (GQ), and Global Technical Services (GTS).

This role is intended for a technically deep individual contributor with strong foundations in operations research and production-grade Python software engineering, and demonstrated capability to develop, maintain, and evolve complex analytics codebases. The role requires close collaboration with other developers, disciplined use of CI/CD and modern engineering workflows, and strong judgment in triaging and prioritizing core platform development to maximize business impact. The successful candidate will also use AI-assisted development tools effectively while balancing accelerated prototyping with strong code comprehension and sound technical decision-making.

Key Responsibilities
 

Technical Leadership: Operations Research and Software Engineering

• Apply operations research methods, including MILP (mixed integer linear programming), DES (discrete event simulation), stochastic modeling, and decision analysis under uncertainty, to complex PDS use cases.
• Architect, implement, and maintain complex Python codebases with high standards for modularity, testability, performance, and reliability, including codebases that operationalize MILP and DES models.
• Translate analytical prototypes into robust, production-ready solutions through close collaboration with BI&T, platform teams, and peer developers.

AI-Assisted Development and Engineering Excellence

• Leverage AI coding tools (e.g., Claude Code or similar) to improve development velocity across implementation, refactoring, documentation, and debugging while maintaining engineering rigor and code comprehension.
• Establish and advance software engineering best practices, including CI/CD, code reviews, automated testing, linting, reproducible environments, and technical documentation.
• Expose operations research tools and model workflows through an MCP server to enable secure, reliable agentic interactions and automation.
• Define and maintain technical standards for code quality, maintainability, and reliable deployment across the AD portfolio.

Collaboration and Business Impact

• Partner closely with AD peers and group leaders to identify, triage, prioritize, and deliver analytics solutions aligned to strategic objectives.
• Communicate analytical findings, technical trade-offs, and implementation implications clearly to both technical and non-technical leaders.
• Support decision-making by framing recommendations with data, model assumptions, and quantified uncertainty.

Team Productivity and Mentorship

• Mentor and coach junior team members on operations research, Python engineering, and best practices in analytics delivery.
• Lead peer reviews, design walkthroughs, and knowledge-sharing sessions to strengthen team capability and consistency.
• Manage multiple workstreams with clear scope, milestones, risk awareness, and acc