Senior Engineer - Data Science

Overview

Who we are

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 applicantsfor employment-based visas or any other work authorization forthis role now or in the future. You must have the right to work inthe United States and not require Toyota support or sponsorshipfor 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 flexibilitybenefits’ [also known as I-140 or Adjustment of Statusportability], etc.) now or in the future. You should not apply forthis role if you will require Toyota to assist with immigrationsupport or sponsorship now or in the future.​

This position is based out of the Toyota Motor North America Headquarters in Plano, TX.

Who we're looking for

Toyota's Digital Innovations group within TMNA's Supply Chain organization is seeking a talented Senior Data Scientist to join our development team. This role will focus on implementing and maintaining high-quality data science and AI solutions while collaborating with senior team members to deliver value to our customers.

Reporting to the Senior Manager, Digital Innovations, the person in this role will support the Supply Chain transformation objectives and help accelerate the adoption of modern and emerging technologies and platforms.

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 you should expect to move between teams and across different parts of the Supply Chain as those priorities shift.

What you'll be doing

Implementing data science and AI solutions that align with architectural decisions, responsible AI practices, and enterprise standards.

Designing, developing, and maintaining reproducible data preparation, feature-engineering, modeling, and scoring workflows using Python, SQL, Databricks, and approved cloud services.

Implementing and optimizing statistical, machine learning, forecasting, optimization, anomaly detection, natural language processing, and generative AI approaches that convert Supply Chain data into useful decision support.

Working with Business Product Owners to refine requirements, analytical questions, measures of success, and acceptance criteria.

Applying data quality, model validation, evaluation, explainability, and error-analysis techniques to ensure the accuracy, reliability, and appropriate use of delivered solutions.

Troubleshooting and resolving data, model, and application issues to ensure smooth project execution and dependable production operation.

Collaborating with cross-functional teams to integrate models, analytical services, dashboards, and AI capabilities into the product framework.

Ensuring non-functional requirements — security, privacy, performance, scalability, monitoring, and system integrations — are met through effective design and development.

Using AI as an assistant across your daily work — analysis, coding, code review, testing, debugging, and documentation — to deliver better outcomes faster, while holding quality and review standards.

Prototyping and experimenting to answer open questions quickly: build the smallest thing that tests the idea, measure what it shows, and share what you learned — including when it does not work.

Spending time with the planners, logistics, buyers, supplier development, and operations teams who use what you build, so solutions reflect how the work actually happens.

Bringing new tools, techniques, and emerging technology into the team's day-to-day practice, and helping teammates adopt what proves out.

What you bring

Strong technical expertise in data science, machine learning, statistical analysis, and data integration, with 5+ years of hands-on experience with modern technologies.

5+ years of proven expertise creating end-to-end data science or machine learning solutions that handle large data volumes and support Supply Chain decisions, business analytics, or operational products.

3+ years of hands-on experience with Python, SQL, data preparation, model development, and reproducible analytical workflows.

Proficiency in supervised and unsupervised learning, time-series forecasting, anomaly detection, feature engineering, model evaluation, and data science libraries such as pandas, NumPy, scikit-learn, PySpark, PyTorch, TensorFlow, or equivalent tools.

Proficient understanding and practical experience with cloud-based data and machine learning technologies — AWS, Apache Spark, Databricks, AWS Redshift, S3 — and orchestration tools such as Apache Airflow / AWS Step Functions.

Hands-on experience with technical design, model and application development, conducting code reviews, and driving code quality.

Experience monitoring data quality, model performance, application health, and error notifications using tools such as Datadog / CloudWatch, Grafana, or comparable platforms.

Proven experience packaging and deploying analytical or machine learning capabilities through batch pipelines, APIs, dashboards, or workflow applications, with appropriate security and release practices.

Strong understanding of DevOps and MLOps practices, CI/CD pipelines, agile methodologies, and version control systems (Git).

Proven ability to work through technical challenges and complex problem-solving to deliver high-quality products, digging to root cause rather than treating symptoms and understanding the business problem before reaching for a solution.

Experience with both functional and non-functional requirements, including security, privacy, performance, scalability, explainability, and responsible AI considerations.

Excellent communication skills, with the ability to collaborate effectively across cross-functional teams.

Demonstrated use of AI tools as an assistant in your data science workflow, with the judgment to know where they help, where they do not, and how to keep quality and review standards intact.

Curiosity about emerging technology and a willingness to experiment — trying new tools and approaches, learning quickly from what does not work, and sharing what you find with the team.

Comfort working as part of a cross-functional fusion team and moving across different parts of the Supply Chain as priorities change.

Added bonus if you have

Experience with optimization, simulation, operations research, or scenario planning for cost, sourcing, supplier, inventory, logistics, or capacity decisions.

Experience building AI/ML data pipelines, feature-engineering workflows, and scalable data products for model development.

Experience preparing structured and unstructured data for model training, inference, document intelligence, and automation use cases.

Familiarity with AI concepts — machine learning, generative AI, prompt engineering, model evaluation, AI governance — and responsible-AI considerations.

Experience using GitHub Copilot within secure development workflows while maintaining code quality and review standards.

Demonstrated experience transforming large-scale applications or modernizing analytical products.

AWS Certified Machine Learning Engineer (Associate), AWS Certified Data Engineer (Associate), Databricks, or equivalent cloud certifications.

Supply Chain, manufacturing, logistics, procurement, supplier development, planning, or supplier risk experience.

Experienc