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Manager Machine Learning Finance Jobs in Rayne, LA

... machine learning, advanced analytics, and generative AI. You will also have a strong product ... Our clients are enterprises as diverse as sophisticated financial institutions and start-ups ...

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Senior AWS Cloud Architect

Lafayette, LA · On-site

$61.75 - $81.25/hr

... performance and cost management. • Participate in Agile planning and provide architecture ... AWS Machine Learning * AWS Machine Learning * Cloud architecture * Docker * GitLab What you can ...

Machinist (Manual)

Opelousas, LA · On-site

$17 - $23/hr

We seek the trust and cooperation of our customers, employees, and community by managing our ... Tuition reimbursement and continuous learning opportunities to support your professional growth.

Financial Advisor

Lafayette, LA · On-site

$137K - $250K/yr

Overview Becoming a financial advisor at Northwestern Mutual is a unique opportunity to start a ... Excellent time-management skills * Desire for continuous learning and collaboration * Proficient ...

Financial Advisor

Lafayette, LA · On-site

$61K - $250K/yr

Overview Becoming a financial advisor at Northwestern Mutual is a unique opportunity to start a ... Excellent time-management skills * Desire for continuous learning and collaboration * Proficient ...

Financial Advisor

Lafayette, LA · On-site

$61K - $250K/yr

Overview Becoming a financial advisor at Northwestern Mutual is a unique opportunity to start a ... Excellent time-management skills * Desire for continuous learning and collaboration * Proficient ...

Overview Becoming a financial advisor at Northwestern Mutual is a unique opportunity to start a ... Excellent time-management skills * Desire for continuous learning and collaboration * Proficient ...

Financial Advisor

Lafayette, LA · On-site

$137K - $250K/yr

Overview Becoming a financial advisor at Northwestern Mutual is a unique opportunity to start a ... Excellent time-management skills * Desire for continuous learning and collaboration * Proficient ...

Financial Advisor

Lafayette, LA · On-site

$61K - $250K/yr

Becoming a financial advisor at Northwestern Mutual is a unique opportunity to start a business ... Excellent time-management skills * Desire for continuous learning and collaboration * Proficient ...

Spend time with your Manager and Regional Vice President learning about responsibilities at their levels to understand the promotional path to see what your career potential is with Republic Finance.

Lead AI Engineer - AWS Platform

Iowa, LA · On-site +1

$130K - $190K/yr

Build machine learning models that automate their training, validation, monitoring, and retraining ... Manage model versioning, performance monitoring, and retraining processes Build on AWS * Develop ...

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Manager Machine Learning Finance information

See Rayne, LA salary details

$34.9K

$103.3K

$140.4K

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

As of Aug 11, 2026, the average yearly pay for manager machine learning finance in Rayne, LA is $103,271.00, according to ZipRecruiter salary data. Most workers in this role earn between $78,500.00 and $139,500.00 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.

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 requires strong programming skills, knowledge of financial markets, and experience with tools like Python, R, or specialized ML platforms. It is widely used in finance to automate processes, detect fraud, and enhance decision-making.

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.

Is manager machine learning finance a high paying job?

Manager roles in machine learning within finance are typically high-paying due to the specialized skills required, such as expertise in data science, programming, and financial modeling. Salaries often reflect experience, location, and the complexity of projects, with many positions offering competitive compensation packages. Certifications and advanced degrees can also influence earning potential.

Sr Machine Learning Engineering Manager - AI Quality and Governance

Workiva Inc.

Iowa, LA • On-site

$193 - $308/hr

Other

Retirement

Posted 6 days ago


Workiva rating

9.9

Company rating: 9.9 out of 10

Based on 7 frontline employees who took The Breakroom Quiz

1st of 242 rated software companies


Job description

Join Workiva as a Sr Machine Learning Engineering Manager - AI Quality and Governance and help establish how we build, evaluate, release, and operate trustworthy AI products at scale. You will lead a multidisciplinary team of software, machine learning, and quality engineers responsible for two connected missions: advancing end-to-end quality across Workiva's AI platform and products, and building shared evaluation and governance capabilities that make our AI systems measurable, observable, reliable, and ready for enterprise use. Your team's scope spans generative AI and agentic products, including AI platform services, agent frameworks and runtimes, conversational experiences, and RAG/knowledge systems. You will partner across Product, Engineering, Data Science, Security, Risk, and Legal to establish practical quality standards and embed evaluation and governance throughout the AI development lifecycle.

What You'll DoLeadership & Team Development
  • Lead, mentor, and develop a multidisciplinary team of software, ML, and quality engineers
  • Build a culture of technical excellence, quality ownership, experimentation, and continuous improvement
  • Establish clear team priorities while balancing platform investments, product needs, and enterprise risk
  • Recruit engineers with complementary expertise across software quality, ML evaluation, platform engineering, and governance automation
AI Product Quality
  • Define and drive a comprehensive quality strategy for Workiva's AI platform and products, spanning unit, integration, end-to-end, performance, resilience, security, and production testing
  • Establish measurable quality bars, release-readiness criteria, and automated quality gates for AI and agentic capabilities
  • Advance testing approaches for nondeterministic systems, including RAG pipelines, agents, prompts, models, tools, and multi-step workflows
  • Detect regressions, model or data drift, unsafe behavior, and degraded customer experiences before and after release
AI Evaluation Platform
  • Lead architecture and delivery of a scalable, self-service evaluation platform for generative AI, RAG, and agentic systems
  • Enable teams to create, manage, version, and reuse evaluation datasets, golden test sets, task-specific metrics, graders, and benchmarks
  • Support deterministic checks, statistical metrics, model-based graders, human evaluation, adversarial testing, and domain-expert review
  • Build capabilities for offline evaluation, pre-release regression testing, online experimentation, production sampling, and continuous evaluation
  • Ensure evaluation results are reproducible, explainable, actionable, and integrated into developer workflows, CI/CD pipelines, and operational dashboards
AI Governance & Assurance
  • Translate Workiva's Responsible AI principles into practical engineering controls and platform capabilities
  • Build governance into the AI lifecycle through traceability, lineage, versioning, documentation, risk classification, approval workflows, and auditable evidence
  • Partner with Security, Legal, Privacy, Compliance, and Risk teams to define controls that support enterprise and regulated use cases
  • Enable inventories and traceability across models, prompts, datasets, evaluations, tools, knowledge sources, and deployed AI features
Cross-Functional Leadership
  • Collaborate with Product, Program Management, UX, UXR, Data Science, Security, Legal, Risk, and engineering leaders to define quality expectations and roadmaps
  • Influence engineering teams across Workiva to adopt shared evaluation standards, testing practices, observability, and release controls
  • Communicate complex technical tradeoffs, quality signals, and risk findings clearly to technical and non-technical audiences
Operational Excellence
  • Ensure the evaluation and governance platform is secure, scalable, reliable, observable, and cost-effective
  • Define service-level objectives and meaningful operational and quality metrics
  • Champion production readiness, incident response, root-cause analysis, and continuous operational improvement
What You'll Need
  • Bachelor's degree in Computer Science, Engineering, Data Science, or related field (or equivalent experience)
  • 10+ years in software engineering, ML engineering, quality engineering, or related roles, including 4+ years leading an engineering team
  • Strong software engineering and systems-design fundamentals, with experience delivering and operating production SaaS or platform capabilities
  • Demonstrated experience establishing automated quality practices for distributed, cloud-based products
  • Practical understanding of the generative AI development lifecycle and challenges of evaluating nondeterministic systems
  • Experience with generative AI concepts: LLMs, RAG, embeddings, vector/hybrid search, agents, tool use, and prompt orchestration
  • Experience defining measurable quality criteria using data, experimentation, telemetry, and production signals
  • Experience with cloud-native architectures on AWS, Azure, or GCP.
  • Proven ability to lead senior individual contributors, navigate tehhnical disagreements, and build high-performance cultures
  • Strong communication and cross-functional leadership skills
Preferred Qualifications
  • Master's degree in Computer Science, Engineering, ML, Data Science, or related field.
  • Experience building or operating AI/ML evaluation, experimentation, observability, model-governance, or ML platform capabilities
  • Experience evaluating RAG and agentic systems, including retrieval quality, groundedness, task completion, tool use, and safety
  • Familiarity with evaluation techniques: golden datasets, statistical metrics, model-based graders, human evaluation, red teaming, A/B testing, and drift/regression detection
  • Working knowledge of ML/AI lifecycle practices: dataset management, model/prompt versioning, experiment tracking, deployment, monitoring, and feedback loops
  • Experience translating Responsible AI, model-risk, privacy, security, or regulatory requirements into scalable engineering controls
  • Familiarity with AI risk/governance frameworks (NIST AI RMF, ISO/IEC 42001, or comparable)
  • Experience with Kubernetes, microservices, CI/CD, infrastructure as code, and modern DevOps/MLOps practices
  • Experience supporting enterprise software in regulated or high-assurance environments
Working Conditions
  • Willingness to travel up to 15% for team and corporate meetings
  • Reliable internet access for remote work
How You'll Be Rewarded
  • Salary range in the US: $193,000.00 - $308,000.00
  • A discretionary bonus typically paid annually
  • Restricted Stock Units granted at time of hire
  • 401(k) match and comprehensive employee benefits package

The salary range represents the low and high end of the salary range for this job in the US. Minimums and maximums may vary based on location. The actual salary offer will carefully consider a wide range of factors, including your skills, qualifications, experience and other relevant factors.

Why Join Workiva Workiva is the platform designed to bring confidence, control, and a competitive edge to the world’s most complex organizations. Our AI-powered platform unifies finance, risk, and sustainability on a single, secure foundation—ensuring data is trusted, traceable, and ready to act on. With an unbroken path from source to output, leaders gain confidence in their numbers, visibility into current and emerging risks, and the ability to move with speed and precision in a constantly changing world. At Workiva, you’ll bring technology to market that executives, boards, and regulators depend on. The work you do here helps organizations navigate uncertainty, maintain trust, and make decisions that stand up to scrutiny. If you’re energized by meaningful challenges, inspired by collaborative teams, and motivated to help organizations turn uncertainty into advantage, we’d love to meet you.

Employment decisions are made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other protected characteristic. Workiva is committed to working with and providing reasonable accommodations to applicants with disabilities. To request assistance with the application process, please email talentacquisition@workiva.com.

Workiva employees are required to undergo comprehensive security and privacy training tailored to their roles, ensuring adherence to company policies and regulatory standards.

Workiva supports employees in working where they work best - either from an office or remotely from any location within their country of employment.

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