Senior Engineer - Data Science

Toyota•Plano, TX

5d•Onsite

About The Position

Toyota's Digital Innovations group within TMNA's Supply Chain organization is seeking a Senior Data Scientist to implement and maintain high-quality data science and AI solutions. The role supports Supply Chain transformation objectives and adoption of modern and emerging technologies and platforms, reporting to the Senior Manager, Digital Innovations. The position is part of cross-functional fusion teams comprising engineers, product team members, and Supply Chain operators who share goals and backlogs. Team assignments may shift across teams and areas of the Supply Chain as priorities change. The role is based at Toyota Motor North America Headquarters in Plano, Texas. The successful candidate will develop and support data science and AI solutions in line with architectural decisions, responsible AI practices, and enterprise standards. The work includes reproducible data preparation, feature engineering, modeling, scoring, analytical and machine learning methods, integrations, production operation, and collaboration with product owners and end users. Toyota does not offer immigration-related employment visa or work authorization support or sponsorship for this role; applicants must already have the right to work in the United States without such support now or in the future.

Requirements

Strong technical expertise in data science, machine learning, statistical analysis, and data integration, with 5 or more years of hands-on experience with modern technologies.
At least 5 years of proven experience creating end-to-end data science or machine learning solutions handling large data volumes and supporting Supply Chain decisions, business analytics, or operational products.
At least 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.
Practical understanding and experience with cloud-based data and machine learning technologies, including AWS, Apache Spark, Databricks, AWS Redshift, and S3, and orchestration tools such as Apache Airflow or AWS Step Functions.
Hands-on experience with technical design, model and application development, code reviews, and driving code quality.
Experience monitoring data quality, model performance, application health, and error notifications using Datadog, CloudWatch, Grafana, or comparable platforms.
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 such as Git.
Ability to solve complex technical problems, identify root causes, and understand the business problem before selecting a solution.
Experience with functional and non-functional requirements, including security, privacy, performance, scalability, explainability, and responsible AI considerations.
Excellent communication skills and ability to collaborate effectively across cross-functional teams.
Demonstrated use of AI tools as assistants in data science workflows, with sound judgment about their use and how to maintain quality and review standards.
Curiosity about emerging technology and willingness to experiment, learn from unsuccessful approaches, and share findings with the team.
Comfort working in a cross-functional fusion team and moving across Supply Chain areas as priorities change.
Must have the right to work in the United States without Toyota immigration-related employment visa or work authorization support or sponsorship now or in the future.

Nice To Haves

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 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.
Experience in Supply Chain, manufacturing, logistics, procurement, supplier development, planning, or supplier risk.
Experience building with LLMs, retrieval-augmented generation, or AI agents in production data and application workflows, including retrieval, tool calling, evaluation, or agentic pipelines.

Responsibilities

Implement data science and AI solutions consistent with architectural decisions, responsible AI practices, and enterprise standards.
Design, develop, and maintain reproducible data preparation, feature-engineering, modeling, and scoring workflows using Python, SQL, Databricks, and approved cloud services.
Implement and optimize statistical, machine learning, forecasting, optimization, anomaly detection, natural language processing, and generative AI approaches for Supply Chain decision support.
Work with Business Product Owners to refine requirements, analytical questions, success measures, and acceptance criteria.
Apply data quality, model validation, evaluation, explainability, and error-analysis techniques to ensure solutions are accurate, reliable, and appropriately used.
Troubleshoot and resolve data, model, and application issues to support project execution and dependable production operation.
Collaborate with cross-functional teams to integrate models, analytical services, dashboards, and AI capabilities into the product framework.
Ensure security, privacy, performance, scalability, monitoring, and system integration requirements are addressed through design and development.
Use AI as an assistant for analysis, coding, code review, testing, debugging, and documentation while maintaining quality and review standards.
Prototype and experiment to test ideas, measure results, and share learnings, including unsuccessful results.
Spend time with planners, logistics, buyers, supplier development, and operations teams to ensure solutions reflect actual workflows.
Introduce emerging tools and techniques into team practices and help teammates adopt approaches that prove effective.

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 retirement contribution regardless of whether the employee contributes (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)