Principal Engineer II - Data Science

Location Plano, Texas, 75024 Category Technology, Data & Analytics Job Id 10338140 Posted Date 10/19/2026 Collaborative. Respectful. A place to dream and do. These are just a few words that describe what life is like at Toyota. As one of the world’s most admired brands, Toyota is growing and leading the future of mobility through innovative, high-quality solutions designed to enhance lives and delight those we serve. We’re looking for talented team members who want to Dream. Do. Grow. with us. 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, ‘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 the Toyota Motor North America Headquarters in Plano, TX. Toyota's Digital Innovations group within TMNA's Supply Chain organization is seeking a highly experienced Principal Data Scientist to lead the technical strategy and execution of Supply Chain data science and AI initiatives. This is a technical leadership role that combines deep data science expertise, architectural leadership, and accountability for delivery across complex data, analytics, and software portfolios. As a Principal Data Scientist, you will shape technical approaches, lead complex modeling and product decisions, and guide execution across development teams in alignment with business priorities. While this role is grounded in principal-level technical leadership, it also requires the ability to develop technical talent, influence delivery practices, and step into direct leadership across one or more technical teams when needed. You will partner with engineering leaders, enterprise architects, product teams, and business stakeholders to deliver reliable decision-support solutions, remove execution barriers, and drive measurable business impact, while advancing AI-enabled delivery capabilities that improve productivity, accelerate learning, and strengthen solution quality. This position reports to the Senior Manager, Digital Innovations and plays a critical role in advancing data science standards, accelerating digital transformation, and scaling delivery excellence across Supply Chain products. You will work as part of a fusion team — engineers, product, and Supply Chain operators working to a shared goal and a shared backlog, rather than a technology group taking requests from a business group. This is how Digital Innovations operates. Technical team members are assigned to fusion teams based on priority and need and move between teams and across different parts of the Supply Chain as those priorities shift; in this role you will help set teams up to succeed and keep data science practices consistent as people move. What you’ll be doing Define and advance the technical approach for Supply Chain data science and AI products built on AWS, Databricks, and Azure, ensuring alignment 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 full 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 elevate technical excellence and strengthen data science capability across the organization. When needed, directly lead technical work across development teams by setting priorities, coordinating execution, and establishing effective delivery rhythms across parallel workstreams. Drive disciplined execution through scaled agile delivery practices — clear user stories, defined acceptance criteria, effective dependency management, risk mitigation, and measurable outcomes across the data science product lifecycle. Work side by side with product, operations, architecture, and platform partners to turn Supply Chain priorities into actionable technical delivery plans for spend and cost analysis, supplier risk, demand forecasting, scenario planning, document intelligence, and AI-enabled decision support. Lead rigorous technical discussions and challenge model, data, and product decisions to ensure solutions meet business objectives, technical feasibility, responsible AI, data governance, and operational excellence standards. Establish and champion modern data science and MLOps practices — reproducible experimentation, model validation, monitoring, deployment automation, observability, reliability engineering, and high-quality software delivery standards. Identify cross-team dependencies and organizational bottlenecks, and drive alignment and decision-making across data science, engineering, product, and stakeholder groups. Provide senior leadership with clear visibility into technical approaches, delivery health, model performance, risks, tradeoffs, and key decisions. Set the standard for AI-assisted data science: use AI as an assistant in your own work, and define where and how teams apply it to improve efficiency, quality, and outcomes — with clear expectations for validation, security, and review. Decide what is worth testing and create the room for the team to test it — running time-boxed experiments and prototypes against models, data, tooling, and emerging technology, and turning what proves out into reusable patterns. Stay close to the operation. Spend time with the buyers, planners, supplier development teams, and other Supply Chain partners who depend on these products so modeling and product decisions reflect how the work actually runs. Champion a culture of accountability, curiosity, experimentation, continuous learning, technical excellence, and inclusive leadership across the data science organization. What you bring 8+ years designing and delivering complex enterprise data science, machine learning, analytics, or software solutions, including large-scale transformation initiatives. 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. 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 effectively 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 your own data science work, and experience raising that capability across a team — with the judgment to know where it helps, where it does not, and how to hold quality, security, and review standards. A track record of digging to root cause on hard, ambiguous problems, and of understanding the Supply Chain business problem before reaching for a model or technical solution. Experience leading in a cross-functional team model where technical teams and operations share one goal, and comfort re-forming teams as priorities move. Technical Expertise 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 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 — data preparation, model development, evaluation, inference workflows, forecasting, optimization, document intelligence, and automation use cases — with 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. Leadership Capabilities 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 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, with the ability to articulate data science strategy, model tradeoffs, and delivery status to technical and non-technical audiences. Added bonus if you have 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 innovative 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. What we’ll bring During your interview process, our team can fill you in on all the details of our industry-leading benefits and career development opportunities. A few highlights include: A work environment built on teamwork, flexibility, and respect Professional growth and development programs to help advance your career, as well as tuition reimbursement Team Member Vehicle Purchase Discount Toyota Team Member Lease Vehicle Program (if applicable) Comprehensive health care and wellness plans for your entire family Toyota 401(k) Savings Plan featuring a company match, as well as an annual retirement contribution from Toyota regardless of whether you contribute (if applicable) Paid holidays and paid time off Referral services related to prenatal services, adoption, childcare, schools and more Tax Advantaged Accounts (Health Savings Account, Health Care FSA, Dependent Care FSA) Relocation assistance (if applicable) Belonging at Toyota Our success begins and ends with our people. We embrace all perspectives and value unique human experiences. Respect for all is our North Star. Applicants for our positions are considered without regard to race, ethnicity, national origin, sex, sexual orientation, gender identity or expression, age, disability, religion, military or veteran status, or any other characteristics protected by law. #J-18808-Ljbffr Toyota