Assistant Spotify Data Science Jobs in Dallas, TX

OverviewWho 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 Financial Services is looking for a passionate and highly motivated General Manager, Data Science & Machine Learning. Reporting to the Vice President of Risk, this role will define, develop, deploy, and scale analytical, data science, machine learning, and application capabilities across TFS.

The General Manager leads a large enterprise data science and machine learning organization by setting technical direction, establishing standards for model development and deployment, and ensuring strong governance, compliance, and operational rigor. The position is responsible for delivering reliable, scalable analytical solutions that drive business value, partnering with business leaders to define decision-support capabilities, and building a strong talent pipeline to advance the organization’s technical and leadership capabilities. In addition, this role works closely with business and technology executives to identify, prioritize, and deliver analytics and machine learning initiatives that create meaningful enterprise value. It translates complex business challenges into strategic roadmaps, investment priorities, and measurable delivery plans, while influencing decisions that shape how the enterprise allocates resources, manages risk, and pursues growth opportunities. The role also defines the long-term strategy for data science and machine learning engineering capabilities, including talent, platforms, governance, and business engagement, and represents the organization in executive planning, budgeting, and governance discussions. The position collaborates across risk, audit, compliance, finance, and technology to manage model risk, operational risk, and regulatory exposure.

The person in this role also serves as a subject matter expert on technical requirements and data team needs and is accountable for key decisions across the organization. This includes determining which initiatives to advance based on customer input and partnership, making pricing and strategic decisions as a member of the VPP Working Group, deciding on model implementation as a member of ASOP, and helping establish governance standards for model development as a member of the Model Governance Council. The General Manager is also responsible for decisions related to the promotion of data scientists.

This position is based at our North American headquarters in Plano, Texas. The selected candidate will be expected to reside within commutable distance of this location.

What you’ll be doingLeadership & Team Management

• Lead a unified, 60-person, multi-level enterprise organization spanning data science and machine learning engineering, including senior leaders, managers, senior individual contributors, and technical teams.
• Define and lead a talent strategy for attracting, assessing, hiring, and retaining exceptional technical and leadership talent within the constraints of the enterprise.
• Develop learning programs for Data Science.

Enterprise Strategy & Technical Direction

• Set enterprise standards and technical direction across modeling, experimentation, deployment, monitoring, and governance.
• Ensure analytical and machine learning systems are designed as reliable, auditable, end-to-end decision systems.
• Establish high standards for reproducibility, data quality, code quality, validation, release readiness, and production support.
• Oversee the full progression of work from problem framing and prototype development through production deployment, adoption, and continuous improvement.

Product, Platform & Solution Delivery

• Guide the development of production-grade solutions on modern cloud-based platforms such as AWS and Snowflake.
• Lead delivery of a broad portfolio of analytical assets and applications, ranging from best-in-class predictive decisioning models to end-to-end business solutions with intuitive interfaces, configurable workflows, embedded analytics, reporting, and enterprise system integration.
• Product ownership responsibilities for Pricing.

Business Partnership & Value Creation

• Partner with executives and business leaders to define decision-support capabilities that improve business outcomes, customer experience, and operational effectiveness. These stakeholders can include risk, audit, compliance, finance, and technology to manage model risk, operational risk, and regulatory exposure.

Risk, Compliance & Governance

• Ensure regulatory compliance through the development, deployment, and monitoring of analytical tools. Examples include Fair Lending monitoring, FDIC, and compliance with CECL and IFRS standards in TMCC’s critical accounting estimates.

What you bring

• Graduate degree in Data Science or a closely related field of study.
• Executive technical leadership:15+ years of relevant professional experience in data science, machine learning, or applied analytics, including substantial hands‑on ownership of analytical model development and production machine learning systems.
• Demonstrated success in applying predictive, prescriptive, forecasting, simulation, optimization, and related methods to complex business problems across multiple domains.
• Financial services and regulated environment experience:Significant experience in financial services, including work in regulated decisioning environments and model-driven processes with governance, auditability, and financial or regulatory impact.
• People leadership: people-management experience, including leadership of technical organizations, leadership of managers of managers, coaching senior leaders, and direct management of senior individual contributors. Proven ability to build high-performing teams, strengthen leadership capability, and create environments in which technical talent thrives.
• Production machine learning lifecycle ownership:Demonstrated experience building, deploying, and operating machine learning or optimization systems in production, with accountability across the full lifecycle from design and development through deployment, monitoring, drift management, and retraining in the cloud.
• Programming, cloud, and data platform proficiency:Strong proficiency in Python and SQL, along with hands‑on experience with tools such as R or SAS, cloud platforms such as AWS, GCP, or Azure, and modern data technologies such as Snowflake, Spark, or Databricks.
• Executive presence and enterprise influence:Proven ability to shape strategy, lead cross‑functional prioritization, and translate complex analytical concepts and technical tradeoffs into clear recommendations for executives and senior business leaders.
• Governance mindset:Strong instinct for ensuring that analytical decisions can be demonstrated to be correct, reproducible, explainable, and defensible before deployment in production.

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 (if applicable)
• 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.

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.