Senior Engineer - Data Science

Toyota North America•Plano, TX

7d•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. Reporting to the Senior Manager, Digital Innovations, this role supports Supply Chain transformation and adoption of modern and emerging technologies and platforms. The position is based at Toyota Motor North America Headquarters in Plano, Texas. The role works within cross-functional fusion teams comprising engineers, product staff, and Supply Chain operators. Team assignments may shift across teams and Supply Chain areas as priorities change. The successful candidate will develop reliable, reproducible analytical and AI solutions, work closely with product owners and end users, and help teammates adopt proven tools and techniques. Toyota does not provide immigration-related employment visa or work authorization sponsorship for this role; applicants must already have the right to work in the United States without Toyota support now or in the future.

Requirements

At least 5 years of hands-on experience in data science, machine learning, statistical analysis, and data integration with modern technologies.
At least 5 years of experience 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.
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 tools such as 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 work through technical challenges and complex problems, identify root causes, and understand the business problem before selecting a solution.
Experience addressing 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 an assistant in data science workflows, with judgment about their appropriate 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 as part of 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 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.
Supply Chain, manufacturing, logistics, procurement, supplier development, planning, or supplier risk experience.
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 that align 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 solution accuracy, reliability, and appropriate use.
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 product frameworks.
Design and develop solutions that meet non-functional requirements, including security, privacy, performance, scalability, monitoring, and system integration.
Use AI as an assistant for analysis, coding, code review, testing, debugging, and documentation while maintaining quality and review standards.
Prototype and experiment to answer open questions, measure results, and communicate findings, including when an approach does not work.
Spend time with planners, logistics, buyers, supplier development, and operations teams to ensure solutions reflect how work is performed.
Bring new tools, techniques, and emerging technology 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 annual 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)
Teamwork-, flexibility-, and respect-oriented work environment