Principal Engineer II - Data Science

Toyota•Plano, TX

3d•Onsite

About The Position

Toyota Motor North America’s Digital Innovations group within Supply Chain is seeking a highly experienced data science technical leader to guide the strategy and execution of Supply Chain data science and AI initiatives. The position combines deep data science expertise, architecture leadership, and accountability for delivery across complex data, analytics, and software portfolios. The role shapes technical approaches, leads complex modeling and product decisions, guides development teams, and may take on direct leadership across technical teams when needed. It partners with engineering leaders, enterprise architects, product teams, and business stakeholders to deliver reliable decision-support solutions and measurable business impact while advancing AI-enabled delivery capabilities. The position is based at Toyota Motor North America Headquarters in Plano, Texas, and reports to the Senior Manager, Digital Innovations. It works within fusion teams in which engineers, product staff, and Supply Chain operators share goals and backlogs. Technical team members may move between teams and Supply Chain areas as priorities change; this role helps teams succeed and maintains consistent data science practices. The role advances data science standards, digital transformation, and delivery excellence across Supply Chain products. The work spans the full data science lifecycle, including discovery, problem framing, experimentation, model development, deployment, and operational support. Areas include analytics, forecasting, optimization, machine learning, document intelligence, and AI-enabled decision support. The role establishes modern data science and MLOps practices, mentors technical talent, supports disciplined agile delivery, helps resolve cross-team dependencies, and communicates delivery health and technical decisions to senior leadership. It also sets standards for responsible AI-assisted work and encourages time-boxed experiments that can become reusable patterns. The role stays connected to Supply Chain operators so technical and product choices reflect operational needs. Toyota states that it does not provide immigration support or employment-based visa sponsorship for this role now or in the future. Applicants must already have the right to work in the United States without requiring Toyota's immigration-related support or sponsorship.

Requirements

At least 8 years designing and delivering complex enterprise data science, machine learning, analytics, or software solutions, including large-scale transformation initiatives.
At least 5 years of deep experience with cloud-native data science and machine learning architecture on AWS, Databricks, and Azure, distributed compute, modern integration patterns, and scalable product design.
At least 5 years delivering production data science solutions using technologies such as Python, SQL, PySpark, Databricks, Apache Spark, MLflow, AWS SageMaker or Bedrock, and contemporary MLOps and platform-engineering practices.
Demonstrated success shaping technical approaches, influencing across multiple teams, and partnering with engineering, product, architecture, and business stakeholders.
Ability to balance technical excellence, delivery outcomes, AI-enabled delivery capabilities, responsible AI, and cost optimization in a complex enterprise environment.
Demonstrated use of AI as an assistant in personal data science work and experience raising that capability across a team, with sound judgment about its appropriate use and quality, security, and review standards.
Track record of finding root causes in difficult, ambiguous problems and understanding the Supply Chain business problem before selecting a model or technical solution.
Experience leading in a cross-functional team model where technical teams and operations share a goal, and comfort re-forming teams as priorities change.
Expertise in statistics, machine learning, forecasting, optimization, experimentation, natural language processing, and technical decision-making for scalable data science products.
Ability to lead design discussions spanning cloud-native data and machine learning architectures, feature engineering, model serving, retrieval-augmented generation, AI-agent workflows, data governance, security, observability, reliability, and operational excellence.
Experience designing and implementing AI and ML solutions involving data preparation, model development, evaluation, inference workflows, forecasting, optimization, document intelligence, and automation; familiarity with AWS AI/ML services such as SageMaker and Bedrock.
Demonstrated ability to incorporate cost-optimization principles into data, model, cloud platform, and product decisions.
Experience integrating data, analytics, and AI/ML capabilities into production-grade enterprise applications, workflows, and decision-support platforms.
Experience leading or coordinating multiple data science, machine learning, analytics, or software development teams across complex enterprise initiatives.
Ability to mentor senior data scientists and technical leads, influence without authority, and step into direct leadership when needed.
Strong experience aligning delivery plans, managing dependencies, and driving execution across cross-functional stakeholders and shared priorities.
Ability to lead adoption of AI-enabled data science capabilities across teams with appropriate standards, governance, and enablement.
Excellent communication skills, including the ability to explain data science strategy, model tradeoffs, and delivery status to technical and non-technical audiences.
Must have the right to work in the United States without requiring Toyota immigration support or employment-based visa sponsorship now or in the future.

Nice To Haves

Experience in globally distributed, high-performing teams and asynchronous delivery environments.
Experience leading or contributing to large-scale transformation initiatives.
Experience with Supply Chain, manufacturing, logistics, procurement, supplier development, or fulfillment platforms at enterprise scale.
AWS certifications such as Machine Learning Specialty, Solutions Architect, or Data Analytics Specialty; Databricks certifications.
SAFe or other agile delivery certifications.
Experience developing analytical tooling that improves cross-functional productivity and decision-making effectiveness.
Experience building with LLMs, retrieval-augmented generation, AI agents, or AI-assisted development platforms in production enterprise environments.
Experience introducing emerging data science or AI technology into an enterprise organization, from evaluation and pilot through standards and adoption.

Responsibilities

Define and advance technical approaches for Supply Chain data science and AI products built on AWS, Databricks, and Azure, aligned with business priorities, enterprise architecture, data governance, and scalable delivery objectives.
Lead architecture and design decisions across data science products and development teams, balancing business value, model quality, explainability, resiliency, security, maintainability, and cost optimization.
Provide principal-level technical leadership across the data science lifecycle, from discovery and problem framing through experimentation, model development, deployment, and operational support.
Mentor data scientists, machine learning engineers, technical leads, and engineering managers to strengthen technical excellence and organizational data science capability.
When needed, lead technical work across development teams by setting priorities, coordinating execution, and establishing delivery rhythms across parallel workstreams.
Drive disciplined scaled-agile delivery, including clear user stories, acceptance criteria, dependency management, risk mitigation, and measurable outcomes.
Partner with product, operations, architecture, and platform teams to turn Supply Chain priorities into technical delivery plans for spend and cost analysis, supplier risk, demand forecasting, scenario planning, document intelligence, and AI-enabled decision support.
Lead technical discussions and challenge model, data, and product decisions to ensure business alignment, technical feasibility, responsible AI, data governance, and operational excellence.
Establish and champion data science and MLOps practices, including reproducible experimentation, model validation, monitoring, deployment automation, observability, reliability engineering, and software delivery standards.
Identify cross-team dependencies and organizational bottlenecks and drive alignment and decisions across data science, engineering, product, and stakeholder groups.
Give senior leadership clear visibility into technical approaches, delivery health, model performance, risks, tradeoffs, and key decisions.
Use AI as an assistant in personal work and define how teams apply it, with expectations for validation, security, and review.
Run time-boxed experiments and prototypes involving models, data, tooling, and emerging technology, and turn successful results into reusable patterns.
Engage with buyers, planners, supplier development teams, and other Supply Chain partners to ensure modeling and product decisions reflect operational needs.
Champion accountability, curiosity, experimentation, continuous learning, technical excellence, and inclusive leadership across the data science organization.

Benefits

Professional growth and development programs.
Tuition reimbursement.
Team Member Vehicle Purchase Discount.
Toyota Team Member Lease Vehicle Program, if applicable.
Comprehensive health care and wellness plans for the employee's family.
Toyota 401(k) Savings Plan with company match and an annual retirement contribution regardless of employee contribution, if applicable.
Paid holidays and paid time off.
Referral services related to prenatal services, adoption, childcare, schools, and more.
Tax-advantaged accounts, including Health Savings Account, Health Care FSA, and Dependent Care FSA.
Relocation assistance, if applicable.
A work environment built on teamwork, flexibility, and respect.