Manager, Machine Learning

Toyota Deutschland GmbH; Plano (TX); On-site; USD 150,000 - 210,000; Full time

Job summary

Toyota is hiring an experienced data science leader in Plano, TX to manage a production-grade ML, analytics, and optimization team. You will own delivery, governance, and performance across credit, pricing, collections, treasury, and other functions.

Reporting to the National Manager, Data Science, you will set technical bar, drive fast yet reliable execution, and coach engineers and senior ICs to durable, high-quality systems.

Qualifications

• 7+ years in data science or ML with production ownership.
• 2+ years people-management experience.
• Experience building ML/optimization systems in production.
• Proficient with Python and SQL; cloud platforms experience.
• Experience with MLOps, governance, and production readiness.

Responsibilities

• Lead and develop a high-performing ML/engineering team with coaching and mentorship.
• Set technical direction for architecture, deployment, and data quality.
• Collaborate with data scientists, analysts, and product partners to translate needs.
• Oversee design and implementation of high-throughput services and analytics apps on cloud platforms.
• Define ML engineering roadmap and operating model across teams.
• Raise the engineering bar with reviews, testing, and governance practices.
• Improve processes, reproducibility, and incident response in ML workloads.
• Balance new development with maintenance and technical debt.
• Represent the team in planning and budgeting discussions.

Skills

Leadership

People management

Strategic planning

Cross-functional collaboration

Architectural thinking

Communication

Mentoring

Problem solving

Education

Master's degree in Computer Science, Engineering, Data Science, Statistics, Mathematics, Operations Research, or related field

Tools

AWS

GCP

Azure

Snowflake

Spark

Databricks

Job description

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.

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, '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 to join the team as a people leader, with responsibilities and scope aligned to the candidate's experience, knowledge, interview performance, and unique skill set. 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

• Lead and develop a high-performing team: 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.
• Set technical direction: 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.
• Partner across functions: 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.
• Deliver scalable, reliable systems: 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.
• Define and maintain the ML engineering roadmap and operating model: 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.
• Raise the engineering bar: 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.
• Improve processes and tools: 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.
• Own portfolio delivery: 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, quality, and long-term operating cost, and ensure the team is building the right capabilities in the right order.
• Represent the team: Communicate status, risks, and design decisions to peers and leadership. Contribute to planning and budgeting discussions. Influence strategy outside your reporting line when needed

What you bring

• Master's degree in Computer Science, Engineering, Data Science, Statistics, Mathematics, Operations Research, or a related technical field, or equivalent practical experience. Advanced degree preferred.
• 7+ years of professional experience in data science, machine learning engineering, or applied ML, with hands-on ownership of production systems and data-intensive applications.
• 2+ years of people-management experience, or equivalent experience leading technical teams, with responsibility for coaching, performance feedback, and delivery ownership, along with a track record of developing engineers and mentoring senior ICs to create environments where teams make thoughtful tradeoffs and deliver durable systems.
• Demonstrated experience building, deploying, and operating machine learning or optimization systems in production, with ownership across the full lifecycle from design through monitoring, drift management, and retraining in the cloud.
• Strong proficiency with Python and SQL, along with hands-on experience using cloud platforms such as AWS, GCP, or Azure and modern data technologies such as Snowflake, Spark, or Databricks.
• Experience establishing or improving engineering processes such as code review, design review, spec-driven development, testing strategy, production readiness, monitoring, documentation, and post-incident review to raise team standards.