ZipRecruiter

Log In

1

Model Risk Jobs in Florida (NOW HIRING)

USAA

Data Scientist Senior

Tampa, FL

Develops and deploys models within the Model Development Control (MDC) and Model Risk Management (MRM) framework. * Composes, and assists peers with composing, technical documents for knowledge ...

USAA

Data Scientist Lead

Tampa, FL

Develops and deploys models within the Model Development Control (MDC) and Model Risk Management (MRM) framework. * Composes and peer reviews technical documents for knowledge persistence, risk ...

USAA

Actuary - Auto and Property Modeling

Tampa, FL · On-site +1

$110K - $129K/yr

Knowledge of Model Risk Management, Model Governance, and Regulatory requirements. * US military experience through military service or a military spouse/domestic partner. Compensation range: The ...

next page

Showing results 1-20

All JobsModel Risk JobsModel Risk Jobs in Florida

Model Risk information

What are some typical challenges faced by professionals working in Model Risk, and how can they be addressed?

Professionals in Model Risk often encounter challenges such as ensuring model accuracy, managing regulatory compliance, and effectively communicating complex technical findings to non-technical stakeholders. Addressing these challenges requires a strong understanding of both quantitative modeling and relevant regulations, as well as strong collaboration skills to work with model developers, auditors, and business units. Staying informed about evolving regulatory standards and participating in ongoing training can also help model risk professionals remain effective and add value to their organizations.

What are the key skills and qualifications needed to thrive as a Model Risk Analyst, and why are they important?

To thrive as a Model Risk Analyst, you need a solid background in quantitative analysis, statistics, or finance, often supported by an advanced degree in a related field. Familiarity with model validation tools, programming languages such as Python or R, and regulatory frameworks like SR 11-7 is essential. Strong analytical thinking, attention to detail, and effective communication skills are crucial for evaluating models and presenting findings to stakeholders. These skills ensure model integrity, regulatory compliance, and risk mitigation in financial institutions.

What is the difference between Model Risk vs Model Validation?

AspectModel RiskModel Validation
Primary FocusIdentifying, assessing, and mitigating risks associated with modelsEvaluating and testing models to ensure accuracy and reliability
Required CredentialsQuantitative skills, risk management certifications, industry experienceQuantitative expertise, validation certifications, industry knowledge
Work EnvironmentRisk management teams within financial institutions or firmsModel validation teams, often within risk or model development departments
Industry UsageUsed across banking, insurance, and investment firms to manage model-related risksCommonly employed in financial services to verify model performance

Model Risk focuses on managing the potential negative impacts of models, including errors and misuse, while Model Validation concentrates on testing and confirming the accuracy and robustness of models. Both roles are essential in financial industries to ensure models are reliable and risks are minimized.

What is model risk?

Model risk refers to the potential for adverse consequences resulting from decisions based on incorrect or misused models. In financial institutions, model risk can arise if a model's assumptions are flawed, if the data input is poor, or if the model is applied inappropriately. Managing model risk involves validating models, monitoring their performance, and ensuring that they are used within their intended scope. Effective model risk management helps organizations avoid significant financial losses and comply with regulatory requirements.
What are the most commonly searched types of Model Risk jobs in Florida? The most popular types of Model Risk jobs in Florida are:
What are popular job titles related to Model Risk jobs in Florida? For Model Risk jobs in Florida, the most frequently searched job titles are:
What job categories do people searching Model Risk jobs in Florida look for? The top searched job categories for Model Risk jobs in Florida are:
What cities in Florida are hiring for Model Risk jobs? Cities in Florida with the most Model Risk job openings:
Model Risk jobs near you
Infographic showing various Model Risk job openings in Florida as of June 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.
Director, AI Enablement & Governance

Director, AI Enablement & Governance

Life Extension Foundation Buyers Club Inc

Fort Lauderdale, FL • On-site

$222K - $232K/yr

Full-time

Posted 21 days ago

Job description

The Director of AI Enablement & Governance leads the responsible adoption, scaling, and oversight of artificial intelligence across the organization. This role sits at the intersection of technology, risk, compliance, and business transformation and is tasked with defining and operationalizing AI governance frameworks, enabling business units to deploy AI solutions effectively, and ensuring alignment with regulatory, ethical, and security standards. This leadership position combines deep knowledge of AI/ML with policy development and cross-functional collaboration skills.

Core Duties and Responsibilities:

  • Develop and execute the enterprise AI enablement strategy aligned with business objectives.
  • Partner with business leaders to identify, prioritize, and scale high-impact AI use cases.
  • Establish reusable frameworks, tools, and best practices to accelerate AI adoption.
  • Build and lead internal AI communities of practice to drive knowledge sharing and innovation.
  • Oversee AI solution lifecycle from ideation through deployment and monitoring.
  • Design and implement a comprehensive AI governance framework covering model risk, ethics, privacy, and compliance.
  • Define policies and standards for responsible AI use, including fairness, transparency, and accountability.
  • Establish model validation, monitoring, and audit processes.
  • Ensure compliance with evolving AI regulations (e.g., EU AI Act, U.S. regulatory guidance) and internal policies.
  • Partner with Legal and Security teams to manage AI-related risks and ensure compliance.
  • Collaborate with data teams to ensure high-quality, secure, and compliant data usage.
  • Define standards for model documentation, explainability, and reproducibility.
  • Implement processes for model performance tracking, drift detection, and retraining.
  • Ensure proper governance over third-party AI tools and vendors.
  • Build, mentor, and lead a high-performing AI governance and enablement team.
  • Serve as a trusted advisor to senior leadership on AI opportunities and risks.
  • Drive change management efforts to embed responsible AI practices across the organization.
  • Deliver training and awareness programs on AI governance and ethical use.
  • Evaluate and select AI platforms, tools, and vendors aligned with governance standards.
  • Oversee implementation of AI governance tooling (e.g., model registries, monitoring systems).
  • Ensure integration of governance controls into MLOps and data pipelines.
  • 2 onsite days per week is an essential function of this position
  • Additional duties as needed


Position Requirements:

  • Bachelor’s degree in Computer Science, Data Science, Engineering, or related field (Master’s preferred).
  • 10+ years of experience in AI/ML, data science, or related domains, with leadership experience.
  • Proven experience developing and implementing AI governance frameworks.
  • Strong understanding of machine learning lifecycle, model risk management, and data governance.
  • Familiarity with AI regulations, ethical frameworks, and industry standards.
  • Experience working cross-functionally with technical and non-technical stakeholders.
  • Experience in regulated industries preferred (e.g., finance, healthcare, insurance).
  • Knowledge of frameworks such as NIST AI Risk Management Framework, ISO/IEC AI standards.
  • Experience with MLOps platforms, model monitoring tools, and AI lifecycle management.
  • Strong communication skills with the ability to influence executive stakeholders.