[Hiring] Operations Research Architect @Kinaxis

Role Description

The Operations Research Architect drives the next generation of optimization and algorithmic capabilities within our platform. As part of the Product R&D team, you will lead the development of large-scale mathematical models and high-performance algorithms that solve some of the most complex supply chain problems in the world.

What you will do

• Define and drive the optimization and algorithmic strategy behind large-scale supply chain systems.
• Shape how advanced techniques, including mathematical optimization, heuristics, and hybrid ML/OR approaches, are designed, implemented, and evolved.
• Guide the development of high-performance models that operate on massive datasets and deliver results in seconds.
• Operate as a technical authority across Product R&D, setting direction for modeling approaches, solver strategies, and algorithm design.
• Mentor senior engineers and researchers, elevating the organization’s approach to optimization and performance.
• Foster a culture grounded in analytical rigor and engineering excellence.

Qualifications

• PhD in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, or a related field.
• Extensive experience designing and delivering optimization models and algorithms in production environments.
• Experience in supply chain management, logistics, or large-scale planning systems.
• Deep expertise in mathematical optimization techniques, including linear programming, mixed-integer programming, heuristics, and metaheuristics.
• Proven ability to formulate and solve large-scale, real-world optimization problems, particularly in supply chain or related domains.
• Strong experience with commercial solvers (e.g., Gurobi, CPLEX, Xpress) and performance tuning of optimization models.
• Solid understanding of algorithm design, computational complexity, and numerical performance.
• Proficiency in C++ or similar high-performance programming languages, with strong software engineering fundamentals.
• Familiarity with hybrid approaches combining optimization and machine learning.
• Experience working with large-scale data systems and in-memory or high-performance computing environments.
• Demonstrated ability to influence technical direction and lead complex decision-making across teams.

Nice to Have

• Strong background in advanced optimization techniques (stochastic optimization, robust optimization, decomposition methods).
• Familiarity with machine learning techniques applied to optimization problems.
• Experience building SaaS or distributed systems that integrate optimization engines.
• Exposure to cloud environments and scalable compute infrastructure.
• Track record of thought leadership (publications, patents, conference speaking, or open-source contributions).

Benefits

• Flexible vacation and Kinaxis Days (company-wide day off on the third Friday of every month).
• Flexible work options.
• Physical and mental well-being programs.
• Regularly scheduled virtual fitness classes.
• Mentorship programs, training, and career development.
• Recognition programs and referral rewards.
• Hackathons.