Manager, Machine Learning

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. 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. Who we are - TFS 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. 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, TN, ‘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. Who we’re looking for  Toyota's Data Science department is looking for an experienced technical leader to manage the team that builds and operates production-grade machine learning, analytics, optimization, and decision-support systems. This role leads the engineers behind ML-powered products across credit, pricing, collections, treasury, and other business functions, setting technical direction, owning delivery, and ensuring these capabilities operate as end-to-end decision systems that balance technical performance, business value, operational reliability, and governance. Reporting to the National Manager, Data Science, you will partner with data science and business leaders and cross-functional technology teams to translate business priorities into intelligent, data-driven capabilities. You will set the team's technical bar and delivery rhythm, helping engineers move quickly without compromising quality or operational readiness. You will remain selectively hands-on where your judgment matters most, shaping architecture, challenging assumptions, and guiding high-impact designs while empowering the team to own execution and innovate. Most importantly, you are a people leader who coaches engineers and senior ICs, gives direct and actionable feedback, grows technical ownership, and builds a team environment where engineers produce thoughtful, durable work. What you’ll be doing   Hire, coach, and mentor Machine Learning Engineers and senior engineers. Create intentional development opportunities for both ICs and those who may grow into leadership. Build a culture of ownership, continuous improvement, and constructive feedback. Guide architecture, testing, deployment, observability, drift detection and revalidation, data quality, and production-readiness standards. Treat ML systems differently from ordinary software by designing for model and data drift, champion/challenger evaluation, clear revalidation triggers, strong lineage, and auditability. Steer designs through sharp questions about failure modes, performance, and governance. Collaborate with data scientists, analysts, data engineers, product managers, risk and finance partners, and technology teams to translate business needs, which are often ambiguous or regulated, into clear technical plans. Work with data science leadership to establish clear handoff and validation criteria for prototypes, ensuring that experimental models can be hardened, governed, and deployed efficiently. Drive consensus by framing options, risks, and recommendations in plain language. Oversee the design and implementation of high-throughput services, batch pipelines, optimization and operations research engines, such as MILP, and analytics applications on AWS, Snowflake, or comparable platforms. Evaluate emerging techniques such as generative AI, simulation, or advanced forecasting when they provide measurable business value, and integrate them responsibly with proper governance. Ensure systems meet reliability, reproducibility, auditability, and performance targets. Sequence model development, platform improvements, and reliability work; clarify ownership boundaries between data science, ML engineering, and other technology teams; and balance short-term experimentation with long-term platform leverage. Run design reviews, code reviews, release checklists, and team processes that prioritize maintainability, reproducibility, safety, and audit-ready documentation. Champion responsible AI practices, including model explainability, bias and fairness considerations, and reproducible decision logic. Introduce stronger MLOps practices, including reusable patterns, CI/CD improvements, automated testing, monitoring and alerting, reproducibility checks, and robust incident response. Help build internal frameworks, templates, and golden paths that make high-quality delivery repeatable. Balance new development with maintenance and technical debt. Drive prioritization across domains and stakeholders by weighing business value, urgency, risk, and technical effort. Manage tradeoffs among speed,