Data Scientist

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.

An important part of the Toyota family is Toyota Financial Services (TFS), the finance and insurance brand for Toyota and Lexus in North America. While TFS is a separate business entity, it is an essential part of this world-changing company- delivering on Toyota's vision to move people beyond what's possible. At TFS, you will help create best-in-class customer experience in an innovative, collaborative environment.

This position is based in Plano, TX with a hybrid mix of some in-office time and some remote work.

To save time applying, Toyota does not offer sponsorship of job applicants for employment-based visas or any other work authorization for this position currently.

The TFS Data & Technology (DnT) team is seeking highly motivated people to fill multiple openings as a Data Scientist, to help our growing Data Science practice. This role is ideal for someone who will thrive working at the intersection of computational science, predictive modeling, and business consulting. The ideal candidate will excel at analysis, manipulation, and cleaning of data, building predictive and prescriptive models using a variety of theoretical and computational techniques, extract insights from models to help inform business decisions, and present results and recommendations to various business partners and leaders.

Additionally, the ideal candidate will also possess the technical expertise to help transition models from development to production and deploy them efficiently. If you have a passion for end-to-end data solutions and enjoy working in a dynamic, collaborative setting, you could be the perfect fit for our team.

• Work closely with team members and business partners to identify and prioritize key questions and drive impactful data-based decisions.
• Extract, manipulate, and clean data from diverse sources using SQL and Python, preparing it for detailed analysis and model development.
• Develop and implement sophisticated predictive and prescriptive models using statistical and machine learning techniques. Ensure these models address key business challenges and deliver actionable insights.
• Create clear, compelling data visualizations to effectively communicate findings to both technical and non-technical stakeholders.
• Effectively and efficiently transition models from development to production within the organization’s existing cloud ecosystem.
• Participate actively in project planning and prioritization sessions to ensure data initiatives align with business goals.
• Stay updated with the latest industry trends and tools and integrate this knowledge to improve methodologies and solutions.

• Master’s degree or higher in a relevant analytical field.
• Hands-on experience building and optimizing data solutions using Python.
• Experience solving problems using a variety of statistical and machine learning techniques.
• Hands-on experience using statistical or machine learning frameworks to solve a variety of real-world problems (e.g., stats models, scikit-learn, PyTorch).
• Knowledge of methods like Logistic Regression, Time Series Analysis, GLMs, Mixed Modeling, Multivariate Statistics, Predictive Modeling, Decision Trees, Gradient-Boosted Trees, Random Forests, and Neural Networks.
• Proactive approach to identifying problems and developing innovative solutions.

• Experience with version control systems such as Git Hub, and familiarity with CI/CD practices to streamline model deployment and code management.
• Hands-on experience with cloud-based machine learning platforms (e.g., AWS Sage Maker or Azure ML) to leverage scalable computing resources and tools.
• Demonstrated ability to lead through influence, effectively navigating and prioritizing complex cross-departmental…