Data Scientist - Optimization

Overview

We’re looking for a Data Scientist – Optimization to lead the design, development, and industrialization of advanced optimization solutions supporting integrated vehicle and parts supply chain transformation at Toyota Digital Innovations.

Applicants must have the right to work in the United States and not require Toyota support or sponsorship for immigration‑related employment.

• Lead the development and deployment of mathematical optimization models for integrated supply chain planning, including mixed‑integer programming, linear programming, network flow, constraint programming, heuristics, simulation‑informed optimization, and scenario‑based decision support.
• Use optimization platforms such as Gurobi to formulate, solve, tune, and operationalize complex business problems involving capacity, allocation, sequencing, routing, inventory, production, distribution, and service‑level tradeoffs.
• Translate business objectives, policies, operational constraints, and Toyota‑specific process rules into data‑driven optimization model structures, objective functions, constraints, decision variables, and performance measures.
• Partner with vehicle and parts business leaders to identify high‑value optimization opportunities, define problem statements, quantify value, prioritize use cases, and establish measurable outcomes tied to supply chain efficiency, revenue enablement, cost reduction, service improvement, and customer/dealer experience.
• Manage and coach a team of data scientists, optimization engineers, analysts, and technical contributors; provide direction on solution design, modeling standards, code quality, experimentation discipline, and operational readiness.
• Collaborate with product owners, architects, data engineers, and cloud/platform teams to embed optimization services into digital products, APIs, workflows, and decision‑support tools.
• Develop scalable data pipelines and model inputs using trusted enterprise data sources, including operational vehicle, parts, logistics, demand, production, and dealer/customer data, with a focus on data quality, lineage, and traceability.
• Define model validation approaches, sensitivity analysis, back‑testing methods, benchmarking, explainability, and guardrails to ensure optimization recommendations are accurate, interpretable, stable, and usable by business teams.
• Oversee the transition of optimization solutions from proof‑of‑concept into production, including MLOps/Model Ops practices, monitoring, retraining or re‑optimization strategies, exception handling, release management, and hyper‑care support.
• Establish standards for scenario planning, what‑if analysis, tradeoff visualization, KPI reporting, and executive storytelling to support faster and better business decisions.
• Support Agile delivery practices by defining epics, features, user stories, acceptance criteria, model requirements, test cases, and traceability from business use cases through technical implementation.
• Communicate complex optimization concepts to executive, business, and technical audiences in clear business language; influence alignment, drive buy‑in, and support adoption of new decision processes.
• Continuously evaluate delivered solutions against company standards, budget expectations, operational stability, compliance requirements, model performance, and business value realization.
• Promote Toyota Way behaviors by encouraging genchi genbutsu, respect for people, continuous improvement, fact‑based decision‑making, and collaboration across business and technology teams.

• Bachelor’s degree or higher in Operations Research, Industrial Engineering, Applied Mathematics, Statistics, Computer Science, Data Science, Engineering, Supply Chain Management, or a related field, or equivalent professional experience.
• Demonstrated experience building and deploying optimization models using Gurobi or comparable commercial/open‑source solvers.
• Strong proficiency in Python and common data science/optimization libraries such as pandas, Num Py, Sci Py, Pyomo, OR‑Tools, scikit‑learn, or equivalent tools.
• Experience formulating optimization problems with real‑world constraints,…