Architect, Operations Research

About KinaxisElevate your career journey by embracing a new challenge with Kinaxis. We are experts in tech, but it’s really our people who give us passion to always seek ways to do things better. As such, we’re serious about your career growth and professional development, because People matter at Kinaxis.    In 1984, we started out as a team of three engineers. Today, we have grown to become a global organization with over 2000 employees around the world, with a brand-new HQ based in Kanata North in Ottawa. As one of Canada’s Top Employers, we are proud to work with our customers and employees towards solving some of the biggest challenges facing supply chains today. At Kinaxis, we power the world’s supply chains to help preserve the planet’s resources and enrich the human experience. As a global leader in end-to-end supply chain management, we enable supply chain excellence for all industries, with more than 40,000 users in over 100 countries. We are expanding our team as we continue to innovate and revolutionize how we support our customers. Location This is a remote position. You can work from home and be located anywhere in Canada About the roleThe 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.Vacancy Status This is an existing job vacancy What you will doAs Operations Research Architect you will define and drive the optimization and algorithmic strategy behind large-scale supply chain systems, shaping how advanced techniques, including mathematical optimization, heuristics, and hybrid ML/OR approaches, are designed, implemented, and evolved. You will guide the development of high-performance models that operate on massive datasets and deliver results in seconds.You will operate as a technical authority across Product R&D, setting direction for modeling approaches, solver strategies, and algorithm design. You will mentor senior engineers and researchers, elevate the organization’s approach to optimization and performance, and foster a culture grounded in analytical rigor and engineering excellence.What we are looking forAll aspects of the operations research lifecycle are second nature to you—from problem formulationand mathematical modeling to algorithm design, optimization, and large-scale execution. You arepassionate about solving complex, real-world problems, and you know how to balance mathematicalrigor and practical performance at scale.You are deeply experienced in translating complex supply chain challenges into well-structuredoptimization models and algorithms, and you think naturally in terms of constraints, trade-offs, andobjective functions. You continuously push for better formulations, faster solvers, and more impactfulsolutions that drive measurable outcomes.You are a strong collaborator and communicator, comfortable working with product managers,engineers, and domain experts. You bring clarity to highly complex problems, frame themmathematically, and guide teams toward solutions that balance optimality, scalability, and real-worldconstraints.PhD in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, or arelated 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 ofoptimization 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 HaveStrong 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-sourcecontributions)Work With Impact: Our platform directly hel