Lead - Supply Chain Decision Intelligence & Operations Research

On seeks a Lead in Supply Chain Decision Intelligence & Operations Research in Zürich to apply mathematical rigor and optimization models to global supply chain challenges.

This position is part of the Analytics & Decision Intelligence team, which supports the Global Supply Chain by combining operations research, applied mathematics, and modern analytical technology. The role focuses on applying rigorous quantitative methods to complex decision-making processes across the organization.

Responsibilities

• Create and evaluate optimization, simulation, and scenario models for inventory, capacity, fulfillment, demand-supply balancing, service risk, and product lifecycle decisions.
• Utilize Python, SQL, Hex, and other analytical tools to rapidly develop and validate models alongside planners and business stakeholders prior to industrialization.
• Independently explore, assess, and transform data required for modeling, while documenting assumptions, data quality issues, transformation logic, and validation rules.
• Collaborate with Data Engineering, Data Science, Optimization Engineering, and platform teams to transition successful prototypes into scalable solutions.
• Develop models that enable leaders to stress-test the supply chain, evaluate constrained scenarios, and quantify risks, sensitivities, and downstream impacts.
• Translate complex model outputs into clear recommendations, trade-offs, and actionable insights for planners, directors, and senior leaders.
• Leverage AI-assisted coding, agentic workflows, and generative tools to accelerate data exploration, modeling, scenario development, and documentation.

Requirements

• Strong foundational knowledge 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.
• Extensive experience in optimization, simulation, stochastic processes, probabilistic modeling, 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.

Nice to have

• Experience in supply chain, logistics, manufacturing, retail, or another constrained operational environment.
• Experience with inventory, allocation, capacity, fulfillment, 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.

About the company

• The work powers On’s growth engine and elevates and speeds decision making.