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Formulate decision problems as mathematical optimization models (LP, MILP, MINLP), translating ... Bachelor's Degree in Operations Research, Industrial Engineering, Applied Mathematics, Computer ...

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How much do mathematical optimization operations research jobs pay per year?

As of Sep 1, 2026, the average yearly pay for mathematical optimization operations research in the United States is $93,804.00, according to ZipRecruiter salary data. Most workers in this role earn between $66,500.00 and $115,500.00 per year, depending on experience, location, and employer.

What is mathematical optimization in operations research?

Mathematical Optimization in Operations Research is the process of finding the best solution from a set of feasible options, subject to constraints, by maximizing or minimizing an objective function. It involves formulating real-world problems mathematically and using algorithms to identify optimal or near-optimal solutions. This field is widely applied in logistics, finance, manufacturing, and other industries to improve efficiency and reduce costs. Professionals use techniques such as linear programming, integer programming, and nonlinear optimization to solve complex decision-making problems.

What are some typical challenges faced by professionals in mathematical optimization operations research, and how can they be addressed?

Professionals in Mathematical Optimization Operations Research often encounter challenges such as dealing with large-scale, complex datasets and ensuring that models are both accurate and computationally efficient. Balancing the precision of theoretical models with the practical limitations of real-world data and time constraints is a common hurdle. Collaboration with domain experts and iterative model refinement are essential strategies to address these issues. Additionally, clear communication of technical results to non-technical stakeholders is crucial for successful project implementation.

What are the key skills and qualifications needed to thrive as a mathematical optimization operations research analyst, and why are they important?

To excel as a Mathematical Optimization Operations Research Analyst, you need advanced skills in mathematics, statistics, and analytical modeling, usually supported by a degree in operations research, applied mathematics, or a related field. Familiarity with optimization software (such as CPLEX or Gurobi), programming languages like Python or R, and data analysis tools is typically required. Strong problem-solving abilities, critical thinking, and effective communication help you translate complex data into actionable business solutions. These competencies are crucial for delivering efficient, data-driven strategies that optimize organizational performance and decision-making.
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Mathematical Optimization Operations Research jobs near you
Infographic showing various Mathematical Optimization Operations Research job openings in the United States as of August 2026, with employment types broken down into 84% Full Time, 13% Part Time, 1% Temporary, 1% Contract, and 1% Nights. Highlights an 94% Physical, 2% Hybrid, and 4% Remote job distribution, with an average salary of $93,804 per year, or $45.1 per hour.

Principal Optimization Architect -- Gurobi & Decision Intelligence

Bristlecone

San Jose, CA • On-site

Other

This job post has expired today. Applications are no longer accepted.

Job description

About Company::


Bristlecone is a supply chain and business analytics advisor, serving customers across a wide range of industries. Rated by Gartner as among the top ten system integrators in the supply chain space, we are uniquely positioned to solve contemporary business problems, with supply chain and analytics focus as our advantage. We have been a trusted partner and advisor to many leading, globally recognized companies such as Applied Materials, Exxon Mobil, Flextronics, LSI Logic, Mahindra, Motorola, Nestle, Palm, Qatar Petroleum, Ranbaxy, Unilever and Whirlpool and many other


About the Role


We are seeking an industry-leading, visionary Principal Operations Research Consultant / Optimization Architect to spearhead the design, development, and deployment of enterprise-scale decision intelligence and optimization systems. In this high-impact role, you will bridge the gap between complex algorithmic theory and real-world production execution.


You will act as the technical authority on mathematical modeling, leveraging deep expertise in Gurobi Optimizer to translate multi-faceted business bottlenecks into highly scalable, automated frameworks. This role is a unique blend of strategic solution architecture, elite hands-on mathematical programming, and technical team mentorship.


Location: - This is US based role, should be open to travel on needed basis


🎯 Key Responsibilities


  • Optimization Solution Development: Design, formulate, and implement advanced mathematical optimization models (Linear Programming, Mixed-Integer Programming, Non-Linear Programming) tailored to solve complex, large-scale enterprise constraints.
  • Solver Mastery & Engineering: Execute advanced tuning, parameter configuration, and structural enhancements using Gurobi Optimizer (mandatory) to drive down solve times and maximize compute efficiency. Develop pristine, maintainable code primarily in Python.
  • Operations Research Leadership: Systematically apply deep OR methodologies to automate business processes, including Network Design, Fleet Routing, Logistics, Scheduling, Stochastic Optimization, and custom Heuristics/Metaheuristics.
  • Team Leadership & Mentorship: Direct, manage, and scale a high-performing team of data analysts and Operations Research specialists. Conduct rigorous code reviews and establish structural modeling standards.
  • Production Deployment & Automation: Collaborate with engineering teams to embed core optimization engines into enterprise software architectures. Construct scalable APIs, microservices, and continuous data pipelines for automated model execution.


Required Qualifications

  • Experience: 10–12+ years of progressive professional experience explicitly dedicated to optimization architecture and applied Operations Research.
  • Mandatory Technical Depth: Undeniable, extensive hands-on history delivering multiple real-world, commercial-grade optimization engines driven by Gurobi Optimizer.
  • Programming Skills: Expert proficiency in Python alongside an understanding of math modeling libraries (e.g., Pyomo, PuLP, Gurobipy).
  • Education: Bachelor's, Master's, or PhD in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, or a deeply quantitative field.
  • Leadership Track Record: Proven background in managing or mentoring technical teams and conducting advanced math code reviews.


📩 If this sounds like the right fit for you or someone you know, feel free to reach out or drop your resume in the comments or messages!

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