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Manager Machine Learning Finance Jobs in Texas (NOW HIRING)

Toyota

Manager, Machine Learning

Plano, TX · On-site

An important part of the Toyota family is Toyota Financial Services (TFS), the finance and ... to manage the team that builds and operates production-grade machine learning, analytics ...

Vanguard

Manager, Machine Learning Engineer

Dallas, TX

$101K - $133K/yr

Leads a team of Machine Learning Engineers responsible for designing, building, deploying, and ... Financial Advisory Services (FAS) business objectives. Partners closely with Product, Data Science ...

Apple

Sr. Machine Learning Engineer - Finance

Austin, TX

$184K - $277K/yr

As a machine learning engineer in Finance, you'll play an integral and global role in building the data foundations, services, and platforms used for delivering insights and automating decisions for ...

ExtendMyTeam

Machine Learning Engineer

Austin, TX

Join a high-growth financial technology organization focused on delivering modern digital banking ... Position Summary We are seeking a Machine Learning Engineer to help design, implement, and scale AI ...

Apple

Sr. Machine Learning Engineer - Finance

Austin, TX

$184K - $277K/yr

As a machine learning engineer in Finance, you'll play an integral and global role in building the data foundations, services, and platforms used for delivering insights and automating decisions for ...

Q2

Machine Learning Engineer

Austin, TX

Our mission is simple: build strong and diverse communities through innovative financial technology ... manager RESPONSIBILITIES Design and implement machine learning algorithms and models for various ...

Q2 Software, Inc.

Machine Learning Engineer

Austin, TX · On-site

Our mission is simple: build strong and diverse communities through innovative financial technology ... manager RESPONSIBILITIES • Design and implement machine learning algorithms and models for ...

Avride

Machine Learning Engineer

Austin, TX · On-site

In this role, you will conduct experiments, manage large-scale datasets, and implement deep ... Develop and Optimize Machine Learning Models: Design, implement, and refine deep learning models to ...

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Manager Machine Learning Finance information

What does a Manager of Machine Learning in Finance do?

A Manager of Machine Learning in Finance oversees teams that develop and implement machine learning models to solve financial problems, such as risk assessment, fraud detection, and algorithmic trading. They coordinate with data scientists, engineers, and business stakeholders to ensure models meet regulatory standards and align with company goals. Additionally, they are responsible for project management, mentoring team members, and staying updated with advancements in both finance and artificial intelligence.

What are the key skills and qualifications needed to thrive as a Manager of Machine Learning in Finance, and why are they important?

To thrive as a Manager of Machine Learning in Finance, you need strong expertise in machine learning, statistics, and financial analysis, typically supported by a relevant advanced degree and experience in both data science and finance. Familiarity with programming languages like Python or R, cloud platforms, and machine learning frameworks such as TensorFlow or Scikit-learn is essential, along with knowledge of regulatory compliance systems. Exceptional leadership, strategic thinking, and communication skills set top candidates apart by enabling effective team management and cross-functional collaboration. These skills and qualities are crucial to drive innovative solutions, ensure regulatory adherence, and deliver business value in a complex financial environment.

What is the difference between Manager Machine Learning Finance vs Data Scientist Finance?

AspectManager Machine Learning FinanceData Scientist Finance
Required CredentialsBachelor's or Master's in Computer Science, Data Science, or Finance; certifications in machine learning or data analysisBachelor's or Master's in Data Science, Statistics, or related fields; often includes certifications in data analysis or programming
Work EnvironmentLeads teams, manages projects, collaborates with stakeholders in financeAnalyzes data, develops models, supports decision-making in finance teams
Employer & Industry UsageFinancial institutions, hedge funds, investment firmsFinancial firms, banks, fintech companies

The Manager Machine Learning Finance oversees teams and projects applying machine learning to finance problems, focusing on leadership and strategy. In contrast, Data Scientists in finance primarily analyze data and develop models to support financial decisions. Both roles require strong technical skills, but the manager role emphasizes team management and project oversight.

How does a Manager of Machine Learning in Finance typically collaborate with cross-functional teams?

A Manager of Machine Learning in Finance often works closely with data scientists, software engineers, financial analysts, and business stakeholders. They are responsible for translating business problems into machine learning solutions and ensuring models meet both technical and regulatory requirements. Regular meetings and clear communication are essential, as the manager must align team efforts with organizational goals, facilitate knowledge sharing, and integrate model outputs into financial decision-making processes. Collaboration also involves coordinating with IT for data infrastructure and with compliance teams to uphold data privacy standards.
What are the most commonly searched types of Machine Learning Finance jobs in Texas? The most popular types of Machine Learning Finance jobs in Texas are:
What are popular job titles related to Manager Machine Learning Finance jobs in Texas? For Manager Machine Learning Finance jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Manager Machine Learning Finance jobs in Texas look for? The top searched job categories for Manager Machine Learning Finance jobs in Texas are:
What cities in Texas are hiring for Manager Machine Learning Finance jobs? Cities in Texas with the most Manager Machine Learning Finance job openings:
Manager Machine Learning Finance jobs near you
Infographic showing various Manager Machine Learning Finance job openings in Texas as of July 2026, with employment types broken down into 91% Full Time, 6% Part Time, 2% Contract, and 1% Nights. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution.
Manager, Machine Learning

Manager, Machine Learning

Toyota Motor Sales, U.S.A., Inc.

Plano, TX • On-site

Full-time

Medical, Retirement, PTO

This job post has expired today. Applications are no longer accepted.

Toyota rating

7.2

Company rating: 7.2 out of 10

Based on 862 frontline employees who took The Breakroom Quiz

24th of 44 rated automakers

Job description

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, quality, and long-term operating cost, and ensure the team is building the right capabilities in the right order.
  • 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
  • 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, 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.
  • 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.
  • Experience and knowledge to ask how a decision can be demonstrated to be correct, reproducible, and defensible before shipping, along with comfort operating in environments where models carry audit and regulatory exposure.
  • Experience managing delivery across multiple projects, stakeholders, and business domains, while balancing urgency, risk, compliance, and technical debt.
  • Experience with writing clear design documents, present technical options and tradeoffs, and provide executive-level updates.

Added bonus if you have
  • Master's or higher in a quantitative or technical discipline (CS, Engineering, Data Science, Statistics, Mathematics, Operations Research, etc.).
  • Domain experience in regulated decisioning (lending, insurance, fraud, risk, pricing) and the governance and auditability practices that come with 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 improved engineering quality across teams.
  • AWS Certified Machine Learning Engineer - Associate, Solutions Architect, Developer, or equivalent.

What we'll bring
During your interview process, our team can fill you in on all the details of our industry-leading benefits and career development opportunities. A few highlights include:
  • A work environment built on teamwork, flexibility and respect
  • Professional growth and development programs to help advance your career, as well as tuition
  • reimbursement
  • Team Member Vehicle Purchase Discount
  • Toyota Team Member Lease Vehicle Program (if applicable)
  • Comprehensive health care and wellness plans for your entire family
  • Toyota 401(k) Savings Plan featuring a company match, as well as an annual retirement contribution from
  • Toyota regardless of whether you contribute
  • 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
Our success begins and ends with our people. We embrace all perspectives and value unique human experiences. Respect for all is our North Star. Toyota is proud to have 10+ different Business Partnering Groups across 100 different North American chapter locations that support team members' efforts to dream, do and grow without questioning that they belong.
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.
Have a question, need assistance with your application or do you require any special accommodations? Please send an email to talent.acquisition@toyota.com.

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