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Manager Machine Learning Finance Jobs in Raleigh, NC

RELX

Principal Machine Learning Engineer I

Raleigh, NC

$136K - $252K/yr

Establish CI/CD standards for ML lifecycle management. * Ensure compliance with enterprise data governance and responsible AI standards. Requirements * 10 + years of Machine Learning/Software ...

Deloitte

Finance Analytics & AI Manager

Raleigh, NC

One or more certifications in artificial intelligence, machine learning, Amazon Web Services ... Work you'll do As a Finance Analytics & AI Manager on the Finance Transformation team, you'll work ...

Capgemini

Data Scientist

Cary, NC · On-site

$85.79 - $97.27/hr

... management experience, particularly within the insurance and/or financial services industry ... Design, develop, train, and optimize machine learning and deep learning models for marketing ...

New

CoVar

Proposal Manager

Durham, NC

We develop artificial intelligence and machine learning solutions that help the Department of ... Partner closely with finance staff to ensure budgets and cost volumes are accurate, compliant, and ...

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Showing results 21-40

All JobsMachine Learning Finance JobsManager Machine Learning Finance JobsManager Machine Learning Finance Jobs in Raleigh, NC

Manager Machine Learning Finance information

See Raleigh, NC salary details

$40.8K

$120.9K

$164.3K

How much do manager machine learning finance jobs pay per year?

As of Aug 21, 2026, the average yearly pay for manager machine learning finance in Raleigh, NC is $120,855.00, according to ZipRecruiter salary data. Most workers in this role earn between $91,900.00 and $163,300.00 per year, depending on experience, location, and employer.

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.
Manager Machine Learning Finance jobs near you

Principal Machine Learning Engineer I

RELX

Raleigh, NC

$136K - $252K/yr

Full-time

Re-posted 9 days ago

Job description

About our Team

LexisNexis Legal & Professional, which serves customers in more than 150 countries with 11,800 employees worldwide, is part of RELX (www.relx.com), a global provider of information-based analytics and decision tools for professional and business customers. Our company has been a long-time leader in deploying AI and advanced technologies to the legal market to improve productivity and transform the overall business and practice of law, deploying ethical and powerful generative AI solutions with a flexible, multi-model approach that prioritizes using the best model from today's top model creators for each individual legal use case. The company employs over 2,000 technologists, data scientists, and experts to develop, test, and validate solutions in line with RELX Responsible AI Principles (https://stories.relx.com/responsible-ai-principles/index.html).

About the Role

Do you love collaborating with teams to solve complex technical problems?

We are seeking a Principal Machine Learning Engineer to design, build, and operate scalable AI/ML systems and agentic architectures that support next-generation legal research and analytics products. This role combines deep ML expertise with distributed systems engineering and AI platform development.

You will play a key role in developing enterprise-grade AI systems, including large language model (LLM) infrastructure, retrieval-augmented generation (RAG) pipelines, and autonomous agent frameworks designed for complex large unstructured data.

Responsibilities:

  • Provide architectural direction and code-level guidance.

  • Establish engineering best practices for ML system design, testing, and deployment.

  • Conduct design reviews, performance reviews, and technical roadmap planning.

  • Architect distributed ML systems serving multiple global products.

  • Standardize infrastructure patterns for LLM serving and retrieval systems.

  • Define and implement enterprise-ready agentic frameworks.

  • Architect multi-step reasoning systems.

  • Lead decisions on deterministic workflows vs. autonomous agents.

  • Implement guardrails, safety layers, and traceability mechanisms.

  • Develop evaluation frameworks to measure reasoning quality, hallucination rates, and reliability.

  • Establish CI/CD standards for ML lifecycle management.

  • Ensure compliance with enterprise data governance and responsible AI standards.

Requirements

  • 10 + years of Machine Learning/Software Engineer experience

  • Master's degree or bachelor's degree, computer science degree is highly desirable.

  • Strong software engineering background with experience in building system design, architecting AI feature/products that caters large number of users and deals with large volume of unstructured data

  • Experience with ML deployment to production

U.S. National Base Pay Range: $136,100 - $252,800. Geographic differentials may apply in some locations to better reflect local market rates. This job is eligible for an annual incentive bonus.

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