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Manager Machine Learning Finance Jobs in Texas (NOW HIRING)

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 ...

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 ...

Q2

Machine Learning Engineer

Austin, TX · Hybrid

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 ...

Avride

Machine Learning Engineer

Austin, TX · On-site

The role involves developing and optimizing machine learning models, managing large-scale datasets, and collaborating with cross-functional teams to integrate solutions into real-world applications.

Q2

Machine Learning Engineer

Austin, TX

... fraud while managing the risks inherent to their business. We build and enhance products that ... Our solutions allow financial institutions to focus more of their time and energy on their mission ...

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All JobsMachine Learning Finance JobsManager Machine Learning Finance JobsManager Machine Learning Finance Jobs in Texas

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.

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 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.

Can manager machine learning finance be used in finance?

A Manager of Machine Learning in Finance oversees the development and implementation of machine learning models to improve financial analysis, risk management, and trading strategies. This role involves skills in data science, programming, and finance, and is used to enhance decision-making processes and automate tasks within financial institutions.

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 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 Engineer

Dallas, TX

Vanguard
Photography Services • 1 - 5K employees
Learn more about Vanguardexternal

$101K - $133K/yr

Full-time

Re-posted 9 days ago

Key responsibilities

  • Leads a team of Machine Learning Engineers in designing, building, deploying, and scaling AI/ML solutions.

  • Partners with cross-functional teams to deliver production-grade AI capabilities and ensure alignment with business objectives.

  • Oversees the end-to-end solution delivery, including architecture planning, implementation, deployment, monitoring, and continuous improvement.

Job description

Leads a team of Machine Learning Engineers responsible for designing, building, deploying, and scaling AI/ML solutions that support Financial Advisory Services (FAS) business objectives. Partners closely with Product, Data Science, Architecture, and Technology teams to deliver production-grade AI capabilities with a strong focus on scalability, reliability, governance, and operational excellence. Drives end-to-end solution delivery from architecture planning through implementation, deployment, monitoring, and continuous improvement

Core Responsibilities

  • Provides leadership in hiring, coaching, talent development, performance management, and compensation decisions in accordance with Human Resources policies and procedures.

  • Partners with Enterprise, Solution, and Domain Architects to define AI/ML solution architectures and translate strategic initiatives into executable roadmaps, epics, and engineering workstreams.

  • Leads cross-functional delivery across Product, Data Science, Platform, and Engineering teams, driving solutions from concept through production while ensuring alignment to business objectives and enterprise standards.

  • Establishes engineering practices, reusable frameworks, and platform capabilities that improve scalability, consistency, and delivery efficiency across AI/ML initiatives.

  • Oversees the design, implementation, and evolution of data, feature, and model pipelines to support reliable and scalable AI/ML solutions.

  • Applies expertise in machine learning, statistics, optimization, and experimentation methodologies to operationalize predictive and decision-support capabilities.

  • Evaluates data quality, feature readiness, and model inputs in partnership with Data Science teams to support successful model development and deployment.

  • Drives operational excellence through automation, observability, monitoring, incident management, and continuous improvement practices for production AI/ML systems.

  • Ensures adherence to enterprise governance, security, risk, compliance, and model lifecycle management requirements.

  • Engages business and technology stakeholders to understand objectives, assess opportunities, and translate complex requirements into actionable technical solutions.

  • Supports departmental planning, prioritization, and execution of strategic objectives while balancing delivery commitments, operational needs, and organizational goals.

  • Establishes scalable operating models, support processes, and service standards that enable long-term sustainability of AI/ML products and platforms.

  • Communicates technical strategy, solution recommendations, delivery progress, and business impact to senior technology and business leaders.

  • Participates in special projects and performs other duties as assigned.

Qualifications

  • Undergraduate degree or equivalent combination of training and experience. Graduate degree preferred.

  • Minimum of eight years related work experience.

Special Factors

Sponsorship

Vanguard is not offering visa sponsorship for this position.

About Vanguard

At Vanguard, we don't just have a mission—we're on a mission.

To work for the long-term financial wellbeing of our clients. To lead through product and services that transform our clients' lives. To learn and develop our skills as individuals and as a team. From Malvern to Melbourne, our mission drives us forward and inspires us to be our best.

How We Work

Vanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience.

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