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Director Model Risk Governance Jobs in California

... Directors. * Ensures compliance with federal, state, and industry-specific regulations, while maintaining documentation and evidence required for compliance and risk governance. * Designs and ...

AVP, Risk Management

Vacaville, CA · On-site

$172.02K - $212.47K/yr

... Directors. * Ensures compliance with federal, state, and industry-specific regulations, while maintaining documentation and evidence required for compliance and risk governance. * Designs and ...

Excellent communication skills - able to explain model risk, brief a committee on AI governance ... Self-directed and resourceful - able to build a program from the ground up, prioritize ...

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Director Model Risk Governance information

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

To thrive as a Director of Model Risk Governance, you need deep expertise in quantitative finance, risk management, and model validation, often backed by an advanced degree in a quantitative field and relevant industry experience. Familiarity with risk management frameworks, regulatory standards (e.g., SR 11-7), and proficiency in analytical tools like Python, R, or SAS are typically required. Exceptional leadership, communication, and critical thinking skills help you effectively oversee teams and coordinate with stakeholders across the organization. These competencies are vital to ensure robust model governance, regulatory compliance, and informed risk-based decision-making at the enterprise level.

What are some common challenges faced by a Director of Model Risk Governance, and how can they be addressed?

A Director of Model Risk Governance often encounters challenges such as ensuring consistent model validation across diverse business units, keeping up with evolving regulatory requirements, and fostering effective communication between model owners, validators, and senior management. Addressing these challenges typically involves establishing robust model risk frameworks, maintaining clear documentation, and promoting a culture of transparency and collaboration. Regular training sessions and open forums can help bridge knowledge gaps, while leveraging technology can streamline model inventory and validation processes.

What are Director Model Risk Governance roles?

Director Model Risk Governance roles are senior positions responsible for overseeing and managing the risks associated with financial and predictive models within an organization. These professionals establish and implement model risk management frameworks, ensure compliance with regulatory requirements, and oversee model validation processes. They collaborate with model developers, validators, and business units to identify, assess, and mitigate model risks, as well as report on governance effectiveness to senior management. Their work is crucial in maintaining the reliability and integrity of models used for decision-making and regulatory reporting.

What is the difference between Director Model Risk Governance vs Model Risk Analyst?

AspectDirector Model Risk GovernanceModel Risk Analyst
CredentialsAdvanced degrees (e.g., Master’s, PhD), professional certifications (e.g., FRM, CFA)Bachelor’s or Master’s degree, relevant certifications
Work EnvironmentStrategic oversight, policy development, senior stakeholder engagementData analysis, model validation, risk assessment
Employer & Industry UsageFinancial institutions, banks, asset managersFinancial institutions, risk management teams
Search & Comparison IntentUnderstanding leadership roles in model risk governanceEntry to mid-level model risk roles, analysis tasks

The main difference is that the Director Model Risk Governance focuses on strategic oversight, policy setting, and managing model risk at a senior level, while the Model Risk Analyst handles technical validation, data analysis, and risk assessment tasks. The director role involves leadership and decision-making, whereas the analyst role is more technical and operational.

What are the most commonly searched types of Model Risk Governance jobs in California? The most popular types of Model Risk Governance jobs in California are:
What are popular job titles related to Director Model Risk Governance jobs in California? For Director Model Risk Governance jobs in California, the most frequently searched job titles are:
What job categories do people searching Director Model Risk Governance jobs in California look for? The top searched job categories for Director Model Risk Governance jobs in California are:
What cities in California are hiring for Director Model Risk Governance jobs? Cities in California with the most Director Model Risk Governance job openings:
Cyber AI Governance and Privacy Senior Consultant

Cyber AI Governance and Privacy Senior Consultant

Deloitte

San Francisco, CA

Other

Posted 15 days ago


Deloitte rating

8.1

Company rating: 8.1 out of 10

Based on 86 frontline employees who took The Breakroom Quiz

60th 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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