Data Scientist - Optimization

Overview

Who we are

Toyota does not offer support or sponsorship of job applicants for employment-based visas or any other work authorization for this role now or in the future. You must have the right to work in the United States and not require Toyota support or sponsorship for immigration-related employment (e.g., H-1B, O-1, E-3, H-1B1, TN, F-1 OPT, F-1 STEM OPT, F-1 CPT, TN, (job flexibility benefits) (also known as I-140 or Adjustment of Status portability), etc.) now or in the future. You should not apply for this role if you will require Toyota to assist with immigration support or sponsorship now or in the future.

This position is based out of Toyota North American Headquarters in Plano, TX

Who we’re looking for

Toyota's Digital Innovations organization is seeking a Data Scientist - Optimization to lead the design, development, and industrialization of advanced optimization solutions supporting integrated vehicle and parts supply chain transformation. This role applies mathematical optimization, operations research, data science, and cloud-based engineering practices to help deliver the North American Vehicle Supply Chain vision of providing the right vehicle to the right place at the right time.

The successful candidate will serve as a hands-on technical leader for optimization use cases across demand planning, supply allocation, production and logistics planning, ETA improvement, inventory positioning, scheduling, routing, network design, and decision automation. The role will use commercial optimization platforms such as Gurobi, along with Python-based data science ecosystems and cloud services, to translate complex business constraints into scalable decision models and production-ready products.

Reporting to the General Manager of Supply Chain Transformation, this person will partner closely with business process owners, product owners, application architects, data engineers, platform teams, and executive stakeholders. The role requires strong technical depth, Toyota Way leadership, cross-functional influence, clear communication, and the ability to move advanced analytics solutions from concept to reliable operations.

What you’ll be doing

• 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, transportation, 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, application developers, 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 appropriate 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/ModelOps practices,