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Principal Engineer II - Data Science

Job in Plano, Collin County, Texas, 75086, USA
Listing for: Toyota
Full Time position
Listed on 2026-10-23
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), Data Analyst, Data Scientist, Data Science Manager
Salary/Wage Range or Industry Benchmark: 170000 - 250000 USD Yearly USD 170000.00 250000.00 YEAR
Job Description & How to Apply Below

Location Plano, Texas, 75024 Category Technology, Data & Analytics Job   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  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,…
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