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Manager, Machine LearningTCC Toyota Motor Credit Corporation Company • Plano, Texas, United States
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Manager, Machine Learning

TCC Toyota Motor Credit Corporation Company
  • US
    Plano, Texas, United States
  • US
    Plano, Texas, United States
Jetzt Bewerben

Über

Overview Collaborative. Respectful. A place to dream and do. Toyota is a world’s most admired brand, growing and leading the future of mobility through innovative, high-quality solutions designed to enhance lives and delight those we serve. Toyota Financial Services (TFS) is the finance and insurance brand for Toyota and Lexus in North America. TFS is a separate business entity that is essential to Toyota’s vision of moving people beyond what’s possible. 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. Who We’re Looking For Toyota’s Data Science department seeks 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. Responsibilities Hire, coach, and mentor Machine Learning Engineers and senior engineers. Create intentional development opportunities for both individual contributors 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, treating ML systems differently from ordinary software. 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 ambiguous or regulated business needs into clear technical plans. Work with data science leadership to establish clear handoff and validation criteria for prototypes, ensuring 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 (e.g., 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, sequencing model development, platform improvements, and reliability work. 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, and manage tradeoffs among speed, quality, and long‑term operating cost. Communicate status, risks, and design decisions to peers and leadership. Contribute to planning and budgeting discussions and influence strategy outside your reporting line when needed. Qualifications Bachelor’s degree in Computer Science, Engineering, Data Science, Statistics, Mathematics, Operations Research, or a related technical field, or equivalent practical experience. 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. Demonstrated experience building, deploying, and operating machine learning or optimization systems in production, covering the full lifecycle from design through monitoring, drift management, and retraining in the cloud. Proficiency with Python and SQL, and 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. Experience managing delivery across multiple projects, stakeholders, and business domains while balancing urgency, risk, compliance, and technical debt. Experience writing clear design documents, presenting technical options and tradeoffs, and providing executive-level updates. Bonus: Master’s degree or higher in a quantitative or technical discipline. Domain experience in regulated decisioning (lending, insurance, fraud, risk, pricing) and the governance and auditability practices that accompany it. Advanced MLOps experience: CI/CD, model registries, containerization (Docker, Kubernetes), infrastructure-as-code, automated drift detection, data validation, or deployment governance. Generative AI application experience: LLM-powered workflows, RAG, semantic search, evaluation, guardrails, monitoring, or responsible-AI practices. Experience building reusable internal platforms, frameworks, templates, or golden paths that improve engineering quality across teams. AWS Certified Machine Learning Engineer – Associate, Solutions Architect, Developer, or equivalent certification. Benefits A work environment built on teamwork, flexibility and respect. Professional growth and development programs, including tuition reimbursement. Team Member Vehicle Purchase Discount and Team Member Lease Vehicle Program (if applicable). Comprehensive health care and wellness plans for your entire family. Toyota 401(k) Savings Plan with company match and annual retirement contribution. Paid holidays and paid time off. Referral services related to prenatal services, adoption, childcare, schools, and more. Tax-advantaged accounts: Health Savings Account, Health Care FSA, Dependent Care FSA. Relocation assistance (if applicable). Belonging at Toyota: Toyota values unique human experiences and diverse perspectives. Applicants for our positions are considered without regard to race, ethnicity, national origin, sex, sexual orientation, gender identity or expression, age, disability, religion, military or veteran status, or any other characteristics protected by law.
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  • Plano, Texas, United States

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  • English
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