Principal Engineer II - Data Science

Toyota North America•Plano, TX

1d•Onsite

About The Position

Toyota Motor North America’s Digital Innovations group within its Supply Chain organization is seeking an experienced data science technical leader to shape and execute Supply Chain data science and AI initiatives. Based at Toyota Motor North America Headquarters in Plano, Texas, this role combines deep data science expertise, architectural leadership, and accountability for delivery across complex data, analytics, and software portfolios. The position reports to the Senior Manager, Digital Innovations. The Principal Engineer II defines technical approaches, leads complex modeling and product decisions, and guides development teams in line with business priorities. The role includes developing technical talent, influencing delivery practices, and directly leading one or more technical teams when needed. The person partners with engineering leaders, enterprise architects, product teams, and business stakeholders to deliver reliable decision-support solutions, address execution barriers, and produce measurable business impact. They also advance AI-enabled delivery capabilities to improve productivity, learning, and solution quality. The role works within fusion teams comprising engineers, product staff, and Supply Chain operators who 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 as that happens. The position advances data science standards, digital transformation, and delivery excellence across Supply Chain products, using cloud and data platforms including AWS, Databricks, and Azure.

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 expanding that capability across a team, with sound judgment about appropriate use and quality, security, and review standards.
Track record of investigating root causes of 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 with teams re-forming 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 complex design discussions involving 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, including data preparation, model development, evaluation, inference workflows, forecasting, optimization, document intelligence, and automation use cases; familiarity with AWS AI/ML services such as SageMaker and Bedrock.
Demonstrated ability to incorporate cost optimization 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.
Demonstrated ability to mentor senior data scientists and technical leads, influence without authority, and take on 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 and ability to explain data science strategy, model tradeoffs, and delivery status to technical and non-technical audiences.
Must be authorized to work in the United States without Toyota immigration support or 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 enterprise-scale Supply Chain, manufacturing, logistics, procurement, supplier development, or fulfillment platforms.
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 an 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 on AWS, Databricks, and Azure, aligning them 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.
Provide technical leadership throughout the data science lifecycle, from discovery and problem framing through experimentation, model development, deployment, and operational support for analytics, forecasting, optimization, machine learning, and AI workloads.
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 practices, including clear user stories, acceptance criteria, dependency management, risk mitigation, and measurable outcomes.
Partner with product, operations, architecture, and platform teams to translate 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 promote modern data science and MLOps practices, including reproducible experimentation, model validation and monitoring, deployment automation, observability, reliability engineering, and quality software delivery.
Identify cross-team dependencies and organizational bottlenecks and drive alignment and decision-making among data science, engineering, product, and stakeholder groups.
Give senior leadership visibility into technical approaches, delivery health, model performance, risks, tradeoffs, and key decisions.
Set standards for AI-assisted data science, including defining how teams use AI to improve efficiency, quality, and outcomes while maintaining validation, security, and review expectations.
Select and enable time-boxed experiments and prototypes involving models, data, tooling, and emerging technologies, and turn successful results into reusable patterns.
Stay connected to Supply Chain operations by spending time with buyers, planners, supplier development teams, and other partners so modeling and product decisions reflect operational realities.
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 entire family
Toyota 401(k) Savings Plan with company match and an annual Toyota retirement 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