Lead - Supply Chain Decision Intelligence & Operations Research

In short

As our Lead - Supply Chain Decision Intelligence & Operations Research, you will apply mathematical rigor to complex decisions across On’s global supply chain.

You will design optimization, simulation, and scenario models that help leaders and planners understand constraints, evaluate trade-offs, and make better decisions. You will work across problem framing, data exploration, rapid prototyping, business validation, and the transfer of successful solutions to Technology for scalable implementation.

This role combines Operations Research, applied mathematics, supply-chain expertise, and modern analytical technology. Technology teams own enterprise pipelines, platforms, and production deployment; you will provide the business logic, mathematical models, prototype evidence, and functional requirements needed to scale valuable solutions.

Your mission

• Develop decision models: Design and test optimization, simulation, and scenario models for areas such as inventory, capacity, fulfilment, demand and supply balancing, service risk, and product lifecycle decisions.
• Build rapid prototypes: Use Python, SQL, Hex, and other analytical tools to quickly develop and validate models with planners and business stakeholders before industrialisation.
• Prepare model-ready data: Independently explore, assess, join, and transform data needed for modelling. Document assumptions, data-quality issues, transformation logic, and validation rules.
• Partner with Technology: Work closely with Data Engineering, Data Science, Optimization Engineering, and platform teams to transfer successful prototypes into scalable solutions. Provide clear model specifications, business logic, input and output requirements, test cases, and acceptance criteria.
• Enable scenario planning: Build models that allow leaders to stress-test the supply chain, evaluate constrained scenarios, and quantify risks, sensitivities, and downstream impacts.
• Translate mathematics into decisions: Turn complex model outputs into clear recommendations, trade-offs, and actionable insights for planners, directors, and senior leaders.
• Apply modern technology: Use AI-assisted coding, agentic workflows, and generative tools to accelerate data exploration, modelling, scenario development, and documentation. These tools should augment, not replace, mathematical rigor, validation, transparency, and explainability.

Your story

Essential capabilities

• You bring strong foundations in Operations Research, Applied Mathematics, Industrial Engineering, Management Science, Decision Science, Systems Engineering, Statistics, Physics, or another quantitative discipline.
• 5 to 7 years of relevant experience in industry, consulting, applied research, academia, or a combination of these environments.
• Strong experience in optimization, simulation, stochastic processes, probabilistic modelling, or related quantitative methods.
• Ability to formulate real-world problems mathematically and implement models computationally.
• Proficiency in Python, R, Julia, MATLAB, or an equivalent environment.
• Strong SQL and data-wrangling skills.
• Ability to work independently with complex and imperfect data.
• Strong problem-framing and analytical thinking.
• Ability to explain quantitative results clearly to non-technical stakeholders.
• Experience collaborating across business, analytics, and Technology teams.

Desirable experience

• Experience in supply chain, logistics, manufacturing, retail, or another constrained operational environment.
• Experience with inventory, allocation, capacity, fulfilment, network, or service-level models.
• Familiarity with tools such as Gurobi, CPLEX, Pyomo, PuLP, OR-Tools, JuMP, SciPy, or Hex.
• Experience with simulation, stochastic optimization, or probabilistic forecasting.
• Experience transferring prototypes into production.
• Familiarity with supply-chain metrics such as OTIF, forecast accuracy, bias, inventory coverage, and inventory turns.
• Interest in AI-assisted development and agentic analytical workflows.

What makes you successful

You combine mathematical depth with pragmatism. You are comfortable working with imperfect data, challenging assumptions, and iterating with end users. You know when a simple model is sufficient, when greater sophistication is justified, and when a prototype is ready to scale.

Most importantly, you are motivated by using rigorous quantitative thinking to improve real decisions.