Principal Operations Research Scientist(Gurobi/CPLEX/Xpress)(Python/Java/C++/C#/ Go)(12+years) - 11780

The Impact of Principal Operations Research Scientist to Coupa:

At Coupa, we are building the smartest Business Spend Management (BSM) platform in the world. Our Supply Chain and Sourcing engineering teams develop advanced tools that help global enterprises design resilient supply chains, optimize procurement events, and maximize the value of every dollar spent. The Operations Research team is at the heart of this mission. As a Principal Operations Research Scientist, you will act as the principal hands-on architect for our core optimization engines, driving technical excellence and turning complex mathematical theories into scalable, production-ready enterprise software.

What You’ll Do

• Algorithmic Leadership: Take end-to-end technical ownership of complex

  optimization features. Formulate, prototype, and implement advanced

  mathematical models (MIP, LP, NLP, Network Flow, and custom heuristics) to

  solve strategic sourcing and supply chain challenges.

• Architecture & Scalability: Partner with software architects to integrate

  optimization models into Coupa’s high-throughput, multi-tenant SaaS platform.

  Ensure the code is modular, well-tested, and built to handle massive enterprise

  datasets.

• Solver Tuning & Optimization: Bench-test, profile, and tune commercial solvers

  (e.g., Gurobi, CPLEX, Google OR-Tools) to dramatically improve performance,

  solve times, and memory footprint under strict application SLAs.

• Technical Mentorship: Act as a force multiplier by mentoring senior and junior

  engineers/scientists. Establish code review standards and run design sessions to

  elevate the team's mathematical and engineering practices.

• Cross-Functional Collaboration: Work closely with Product Managers and Senior

  Engineering leaders to define feature requirements, translate business objectives

  into technical specifications, and manage algorithmic trade-offs.

• Empirical Validation: Design robust validation frameworks and simulation

  environments to thoroughly test model behavior, ensuring stability and

  mathematical correctness against highly variable, messy real-world data.

What You Will Bring to Coupa

• Master’s degree or Ph.D. in Operations Research, Industrial Engineering,

  Applied Mathematics, Computer Science, or a closely related field.

• 12+ years of industry experience applying operations research methods and

  deploying production-level optimization models in commercial environments at

  scale.

• Expert knowledge of mathematical programming and deep experience with

  authoring model formulations for commercial solvers (such as Gurobi, CPLEX, or

  Xpress).

• Strong programming proficiency in Python, Java, C++, C# or Go, with a solid

  understanding of software design patterns and object-oriented programming.

• Deep understanding of computational complexity, advanced data structures, and

  algorithmic design.

• Excellent communication skills, with a proven ability to explain highly complex

  mathematical behaviors to product and business leaders.

• You are an elite mathematical modeler who also loves writing clean, production-

  grade code.

• You are highly analytical, self-driven, and excel at converting ambiguous, high-

  level business constraints into structured, elegant algorithms.

• You enjoy mentoring others and are passionate about cultivating a highly

  collaborative, intellectually rigorous engineering culture.

• You embody Coupa’s core values: Ensure Customer Success, Focus on Results,

  and Strive for Excellence.

What Will Give You a Competitive Edge (Preferred Qualifications)

• Deep domain expertise in Supply Chain Network Design, Inventory Optimization,

  Production/Scheduling Optimization, Routing Optimization, Strategic

  Procurement, and/or Logistics.

• Experience utilizing Machine Learning techniques (e.g., forecasting, clustering) in

  tandem with mathematical optimization models.

• Experience with PySpark, SQL, and processing large-scale datasets in

  distributed computing environments.