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Model Risk Jobs in Arizona (NOW HIRING)

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 ...

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 ...

Ensure alignment with model risk management, responsible AI, and data governance requirements * Coordinate documentation, approvals, and governance readiness * Deliver structured updates through ...

Actuary - Auto and Property Modeling

Phoenix, AZ · On-site +1

$115.70K - $136K/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 ...

Actuary - Auto and Property Modeling

Phoenix, AZ · On-site +1

$113K - $132.90K/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 ...

Actuary - Auto and Property Modeling

Phoenix, AZ · On-site +1

$115.70K - $136K/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 ...

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Model Risk information

See Arizona salary details

$13

$28

$68

How much do model risk jobs pay per hour?

As of Jun 2, 2026, the average hourly pay for model risk in Arizona is $28.27, according to ZipRecruiter salary data. Most workers in this role earn between $18.12 and $36.06 per hour, depending on experience, location, and employer.

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 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 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 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 are the most commonly searched types of Model Risk jobs in Arizona? The most popular types of Model Risk jobs in Arizona are:
What cities in Arizona are hiring for Model Risk jobs? Cities in Arizona with the most Model Risk job openings:
Cyber AI Governance and Privacy Senior Consultant

Cyber AI Governance and Privacy Senior Consultant

Deloitte

Gilbert, AZ • On-site

Other

Posted 21 days ago


Deloitte rating

8.1

Company rating: 8.1 out of 10

Based on 86 frontline employees who took The Breakroom Quiz

59th of 138 rated financial services


Job description

We are seeking an AI Governance and Privacy Specialist who can operationalize responsible AI in real systems-especially agentic AI and LLM-enabled applications. This role blends governance and privacy expertise with enough software development fluency to create developer-ready guidance, implement controls-as-code patterns, and stand up measurable evaluation and monitoring workflows.

As a Senior Consultant, you will help clients and internal delivery teams move from AI principles to practices: risk tiering, model and agent inventories, technical guardrails, governance workflows integrated into the SDLC, and evidence artifacts suitable for audits and regulators.

Recruiting for this role ends on 6/5/2026.

Work You'll Do

You will lead and deliver AI governance, privacy, and security outcomes across the AI lifecycle, including:

  • Designing pragmatic AI governance operating models (intake, risk tiering, approvals, documentation standards, exception handling, and audit readiness) with a focus on GenAI and agentic AI deployments.
  • Building and maintaining AI system inventories (models, agents, tools, data sources, integrations), with clear ownership, intended use, risk classification, and change-control expectations.
  • Conducting AI risk assessments for privacy, security, model risk, and misuse-including prompt injection, sensitive data exposure, excessive agency, and overreliance-and translating findings into implementable mitigations.
  • Establishing technical control guidance for teams building agentic AI solutions: human-in-the-loop patterns, tool access controls, safe retrieval and grounding practices, logging/monitoring, token and data minimization, and incident response playbooks.
  • Implementing "governance in the workflow" by integrating governance checkpoints into product and engineering delivery (architecture reviews, release gates, evaluation requirements, documentation automation, and evidence capture).
  • Standing up or enhancing evaluation and monitoring approaches for GenAI systems: test plans, safety and quality metrics, red teaming workflows, and reporting dashboards for leaders and risk stakeholders.
  • Partnering cross-functionally with Cybersecurity, Privacy, Legal, Risk, Engineering, and Data Science to drive adoption and ensure governance guidance is usable, measurable, and repeatable.

The Team

You will join a cross-functional group working at the intersection of cyber, privacy, governance, and emerging AI delivery. The team helps organizations scale AI responsibly by combining governance and engineering patterns so teams can innovate faster without compromising trust.

Qualifications

Required

  • Bachelor's degree or equivalent practical experience.
  • 4+ years of experience in one or more of the following: AI governance, data privacy, security risk management, compliance and controls, AI product risk, model risk management, or technology risk consulting.
  • Demonstrated experience translating policies and regulatory expectations into operational workflows, artifacts, and controls (e.g., intake processes, inventories, decision logs, risk registers, RACI, playbooks).
  • Working knowledge of AI/ML/LLM systems and delivery lifecycles sufficient to assess real deployment risks and mitigations (training vs. RAG vs. fine-tuning vs. tool use, data dependencies, integration patterns).
  • Software development fluency: ability to collaborate with engineering teams on implementation details; ability to prototype or automate governance workflows in Python/SQL and to understand CI/CD and cloud deployment basics.
  • Practical experience with privacy program execution and artifacts (PIAs/DPIAs, vendor reviews, data inventories, data minimization, retention, and access control principles).
  • Ability to communicate clearly with both technical and non-technical stakeholders and produce executive-ready reporting.
  • Ability to travel 0-50%, on average, based on client and project needs.
  • Limited immigration sponsorship may be available.

Preferred

  • Previous consulting or Big 4 experience.
  • Hands-on experience operationalizing AI governance aligned to frameworks such as the NIST AI RMF and/or ISO/IEC 42001, with awareness of risk-based AI regulatory regimes (e.g., EU AI Act).
  • Experience with GenAI safety and evaluation practices (prompt injection testing, jailbreak resilience, hallucination measurement, toxicity/harm scoring, grounding effectiveness).
  • Familiarity with governance tooling and workflow platforms (e.g., OneTrust, GRC platforms, ticketing/workflow systems) and how to integrate them into engineering delivery.
  • Certifications such as CIPP/US, CIPM, IAPP AIGP, CISM, or CISSP.
  • Prior experience in cyber or enterprise security contexts (data security, identity, audit logging, secure SDLC).
  • Experience designing Human-in-the-Loop escalation pathways, exception handling, and automated safety protocols for highly autonomous systems.

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $118,700 - 218,600. 

You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.

#CyberDTP27

Qualifications:

We are seeking an AI Governance and Privacy Specialist who can operationalize responsible AI in real systems-especially agentic AI and LLM-enabled applications. This role blends governance and privacy expertise with enough software development fluency to create developer-ready guidance, implement controls-as-code patterns, and stand up measurable evaluation and monitoring workflows.

As a Senior Consultant, you will help clients and internal delivery teams move from AI principles to practices: risk tiering, model and agent inventories, technical guardrails, governance workflows integrated into the SDLC, and evidence artifacts suitable for audits and regulators.

Recruiting for this role ends on 6/5/2026.

Work You'll Do

You will lead and deliver AI governance, privacy, and security outcomes across the AI lifecycle, including:

  • Designing pragmatic AI governance operating models (intake, risk tiering, approvals, documentation standards, exception handling, and audit readiness) with a focus on GenAI and agentic AI deployments.
  • Building and maintaining AI system inventories (models, agents, tools, data sources, integrations), with clear ownership, intended use, risk classification, and change-control expectations.
  • Conducting AI risk assessments for privacy, security, model risk, and misuse-including prompt injection, sensitive data exposure, excessive agency, and overreliance-and translating findings into implementable mitigations.
  • Establishing technical control guidance for teams building agentic AI solutions: human-in-the-loop patterns, tool access controls, safe retrieval and grounding practices, logging/monitoring, token and data minimization, and incident response playbooks.
  • Implementing "governance in the workflow" by integrating governance checkpoints into product and engineering delivery (architecture reviews, release gates, evaluation requirements, documentation automation, and evidence capture).
  • Standing up or enhancing evaluation and monitoring approaches for GenAI systems: test plans, safety and quality metrics, red teaming workflows, and reporting dashboards for leaders and risk stakeholders.
  • Partnering cross-functionally with Cybersecurity, Privacy, Legal, Risk, Engineering, and Data Science to drive adoption and ensure governance guidance is usable, measurable, and repeatable.

The Team

You will join a cross-functional group working at the intersection of cyber, privacy, governance, and emerging AI delivery. The team helps organizations scale AI responsibly by combining governance and engineering patterns so teams can innovate faster without compromising trust.

Qualifications

Required

  • Bachelor's degree or equivalent practical experience.
  • 4+ years of experience in one or more of the following: AI governance, data privacy, security risk management, compliance and controls, AI product risk, model risk management, or technology risk consulting.
  • Demonstrated experience translating policies and regulatory expectations into operational workflows, artifacts, and controls (e.g., intake processes, inventories, decision logs, risk registers, RACI, playbooks).
  • Working knowledge of AI/ML/LLM systems and delivery lifecycles sufficient to assess real deployment risks and mitigations (training vs. RAG vs. fine-tuning vs. tool use, data dependencies, integration patterns).
  • Software development fluency: ability to collaborate with engineering teams on implementation details; ability to prototype or automate governance workflows in Python/SQL and to understand CI/CD and cloud deployment basics.
  • Practical experience with privacy program execution and artifacts (PIAs/DPIAs, vendor reviews, data inventories, data minimization, retention, and access control principles).
  • Ability to communicate clearly with both technical and non-technical stakeholders and produce executive-ready reporting.
  • Ability to travel 0-50%, on average, based on client and project needs.
  • Limited immigration sponsorship may be available.

Preferred

  • Previous consulting or Big 4 experience.
  • Hands-on experience operationalizing AI governance aligned to frameworks such as the NIST AI RMF and/or ISO/IEC 42001, with awareness of risk-based AI regulatory regimes (e.g., EU AI Act).
  • Experience with GenAI safety and evaluation practices (prompt injection testing, jailbreak resilience, hallucination measurement, toxicity/harm scoring, grounding effectiveness).
  • Familiarity with governance tooling and workflow platforms (e.g., OneTrust, GRC platforms, ticketing/workflow systems) and how to integrate them into engineering delivery.
  • Certifications such as CIPP/US, CIPM, IAPP AIGP, CISM, or CISSP.
  • Prior experience in cyber or enterprise security contexts (data security, identity, audit logging, secure SDLC).
  • Experience designing Human-in-the-Loop escalation pathways, exception handling, and automated safety protocols for highly autonomous systems.

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $118,700 - 218,600. 

You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.

#CyberDTP27

Education:Bachelor's DegreeEmployment Type:

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