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Contract Model Risk Governance Jobs in Plymouth, MA

Ensure responsible and compliant AI adoption, aligned with AI governance, model risk management, data privacy, and security controls . * Guide the integration of AI/ML capabilities into analytics ...

Contract Administration and Management Job Category: People Leader All Job Posting Locations ... governance, risk management, and customer experience. DUTIES & RESPONSIBILITIES 1. Strategy ...

... strategy, operating model, and measurable outcomes for analytics-enabled compliance, risk ... Define governance standards for analytics, advanced analytics, automation, and AI-enabled solutions ...

New

... risk monitoring, and enterprise financial reporting across DePuy Synthes. This leader will ... Experience with advanced analytics, machine learning, GenAI, automation, model governance, or ...

... models. Your work will directly improve how these systems identify risk and interpret contract language to create tools with improved precision and legal judgment. Key Responsibilities: * Perform ...

... models. Your work will directly improve how these systems identify risk and interpret contract language to create tools with improved precision and legal judgment. Key Responsibilities: * Perform ...

... models. Your work will directly improve how these systems identify risk and interpret contract language to create tools with improved precision and legal judgment. Key Responsibilities: * Perform ...

... models. Your work will directly improve how these systems identify risk and interpret contract language to create tools with improved precision and legal judgment. Key Responsibilities: * Perform ...

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Contract Model Risk Governance information

See Plymouth, MA salary details

$10

$49

$155

How much do contract model risk governance jobs pay per hour?

As of Sep 5, 2026, the average hourly pay for contract model risk governance in Plymouth, MA is $49.74, according to ZipRecruiter salary data. Most workers in this role earn between $16.20 and $78.46 per hour, depending on experience, location, and employer.

What is contract model risk governance?

Contract Model Risk Governance refers to the framework and processes used by organizations to identify, assess, monitor, and mitigate risks associated with the use of models in contracts or contractual obligations. This role ensures that the use of quantitative models in financial and business contracts complies with regulatory standards and internal policies, reducing the likelihood of errors, misinterpretations, or financial losses. Professionals in this field often oversee model validation, implementation, and documentation, and work closely with compliance, risk, and legal teams. Effective governance helps maintain model integrity and supports sound decision-making across the organization.

What are the key skills and qualifications needed to thrive in contract model risk governance?

To excel in Contract Model Risk Governance, you need a strong background in risk management, quantitative analysis, and familiarity with regulatory requirements, often supported by a degree in finance, mathematics, or a related field. Proficiency with risk management software, model validation tools, and knowledge of frameworks such as SR 11-7 is typically required. Attention to detail, critical thinking, and effective communication are crucial soft skills for evaluating model risk and collaborating with stakeholders. These skills ensure robust oversight of model risk, regulatory compliance, and support sound decision-making within financial institutions.

What are some common challenges faced by professionals in contract model risk governance roles, and how can they be addressed?

Professionals in Contract Model Risk Governance often encounter challenges such as keeping up with evolving regulatory requirements, ensuring thorough model documentation, and effectively communicating risk findings to both technical and non-technical stakeholders. Balancing the need for detailed model validation with tight project timelines can also be demanding. To address these challenges, it's important to foster strong cross-functional collaboration, stay updated on industry best practices, and develop clear communication strategies for reporting risk and compliance issues.

What is the difference between Contract Model Risk Governance vs Contract Model Validation?

AspectContract Model Risk GovernanceContract Model Validation
Primary FocusOverseeing and managing risks associated with contract models, ensuring compliance and risk mitigationAssessing and testing contract models to ensure accuracy and reliability
ResponsibilitiesEstablishing policies, monitoring risk exposure, and implementing controlsPerforming independent reviews, testing model assumptions, and validating outputs
Work EnvironmentRisk management teams, compliance departments, regulatory interactionsQuantitative teams, model validation units, audit functions

While Contract Model Risk Governance focuses on managing and overseeing risks related to contract models, Contract Model Validation involves the technical assessment and testing of those models to ensure their accuracy and reliability. Both roles are essential in a comprehensive risk management framework within financial institutions and industries relying on contract models.

What cities near Plymouth, MA are hiring for Contract Model Risk Governance jobs?

Cities near Plymouth, MA with the most Contract Model Risk Governance job openings:

Infographic showing various Contract Model Risk Governance job openings in Plymouth, MA as of August 2026, with employment types broken down into 2% As Needed, 81% Full Time, 14% Part Time, 1% Temporary, and 2% Contract. Highlights an 89% Physical, 3% Hybrid, and 8% Remote job distribution, with an average salary of $103,465 per year, or $49.7 per hour.

GDMS Senior AI Governance & Risk Specialist

Phase2 Technology

Taunton, MA • On-site

$144.45 - $152/hr

Other

Posted 18 days ago


Key responsibilities

  • Conduct and lead comprehensive AI risk assessments and governance audits against emerging regulations for generative AI, LLM‑based, and agentic applications.

  • Evaluate and ensure adherence to government and corporate AI policies, standards, and regulations across multiple layers including AI inventory, data governance, security, model assurance, human oversight, and compliance.

  • Assess risks specific to agentic AI systems and multi‑agent architectures, including tool‑calling behavior, memory and retrieval systems, external API access, autonomous decision loops, and agent‑to‑agent communication patterns.


Job description

GDMS Senior AI Governance & Risk Specialist

Basic Qualifications: Bachelor's degree or equivalent, or the combination of education and relevant work experience with a minimum of 8 years of relevant experience; or Master's degree with a minimum of 6 years of relevant experience. U.S. citizenship is required.

Job Description

GDMS operates one of the largest enterprise AI deployments in the defense industry, with adoption spanning generative AI copilots, LLM‑powered applications, and a rapidly growing portfolio of agentic and autonomous AI systems. The governance challenge is keeping pace with a workforce that already uses AI while ensuring every deployment meets risk, security, and compliance standards that mission‑critical defense work demands.

As a Sr. AI Governance & Risk Specialist, you will be a core practitioner on the Agentic AI Governance team, executing day‑to‑day work that keeps GDMS AI deployment safe, accountable, and trusted. Responsibilities include conducting AI risk assessments, performing governance audits, evaluating adherence to regulations, leading corrective actions, and serving as subject matter expert for engineering and program teams navigating the AI lifecycle. You will work directly with agentic tools and applications, bringing firsthand understanding of their behavior and governance controls.

Key Responsibilities AI Governance Execution & Assessment
  • Conduct and lead comprehensive AI risk assessments and governance audits against emerging regulations for generative AI, LLM‑based, and agentic applications; document findings, risk ratings, and mitigation strategies, and lead corrective actions.
  • Evaluate and ensure adherence to government and corporate AI policies, standards, and regulations across the six layers: AI inventory and discovery; data governance; security and access controls; model assurance; human oversight; and compliance and audit.
  • Apply and maintain tiered governance frameworks calibrated to risk level, ensuring low‑risk use cases clear quickly while mid‑ and high‑risk applications receive appropriate scrutiny and escalation.
  • Maintain the enterprise AI use inventory and control framework, including system inventory, risk register, shadow AI detection, approved use catalog, and control mapping; support dashboard reporting and KPI monitoring for AI governance program health.
  • Prepare governance recommendations for approval and escalation, ensuring mid‑ and high‑risk AI systems are escalated with clear risk rationale and decision support materials.
  • Support development of self‑service governance tooling, checklists, and playbooks that enable program teams to adopt AI responsibly without requiring individual review for low‑risk applications.
Agentic AI Risk & Technical Assessment
  • Assess risks specific to agentic AI systems and multi‑agent architectures including tool‑calling behavior, memory and retrieval systems, external API access, autonomous decision loops, and agent‑to‑agent communication patterns.
  • Apply failure mode analysis to evaluate behavioral boundaries, unintended action risks, adversarial prompt vulnerabilities, and out‑of‑scope execution risks for agentic deployments.
  • Evaluate and document human‑in‑the‑loop (HITL) requirements and escalation thresholds appropriate to each agentic use case based on risk level, decision reversibility, and mission context.
  • Conduct hands‑on evaluation of agentic tools and platforms including AI coding assistants, copilot‑style applications, and multi‑agent orchestration frameworks to ground governance assessments in actual system behavior rather than vendor documentation alone.
  • Implement measures to monitor and mitigate risks associated with AI systems and data flows across GDMS IT and network infrastructure; investigate and manage responses to AI governance incidents and anomalies to prevent and mitigate exposure.
Policy, Standards & Regulatory Compliance
  • Maintain AI governance policies for responsible AI deployment, integrating government and corporate AI requirements into policy, standards, procedures, and operational guidance; own the policy lifecycle from drafting through review, approval, and periodic refresh aligned to enterprise risk priorities and evolving regulatory expectations.
  • Translate regulatory requirements, including NIST AI RMF, OWASP Top 10 for LLMs, MITRE ATLAS, the EU AI Act, applicable U.S. Executive Orders on AI, and ISO 42001, into clear, actionable internal controls and assessment criteria without creating bureaucratic drag.
  • Monitor the evolving domestic and international AI regulatory landscape; identify changes with organizational impact and elevate findings with recommended policy responses.
  • Coordinate with Privacy, Legal, Cybersecurity, and IT leadership to assess compliance risk against emerging AI regulations, including the EU AI Act, applicable U.S. Executive Orders on AI, and evolving DoD and federal AI policy; identify control gaps, quantify exposure, and recommend corrective measures before requirements become binding obligations.
  • Produce compliance reporting on AI controls for internal audit, regulatory examination, and governance committee review, documenting control effectiveness, open findings, and remediation status; support audit readiness activities including evidence collection, control validation, and documentation packages suitable for internal and regulatory stakeholders.
Risk Monitoring, Reporting & Controls Assurance
  • Perform ongoing monitoring and validation of deployed AI systems, including review of model performance, drift indicators, bias signals, and continued alignment with approved use scope.
  • Identify opportunities to apply AI and automation for continuous improvement of the AI governance program itself, including automated risk attribution, KPI tracking, and telemetry‑driven evidence.
  • Generate AI risk and governance reporting contributing to dashboards, risk posture summaries, and periodic reports for program leadership and cross‑functional stakeholders.
  • Evaluate effectiveness of cybersecurity controls applied to AI systems (NIST CSF, NIST AI RMF), collaborating with the Cybersecurity organization to integrate governance without duplicating ownership.
  • Support vendor and third‑party AI risk assessments, ensuring AI components from external providers meet GDMS contractual, regulatory, and governance requirements.
Knowledge, Skills & Abilities
  • Collaborates and works effectively cross‑functionally throughout the business, including with Legal, Information Technology, Cybersecurity, Security, and Contracts organizations.
  • Excellent computer and data management knowledge, including IT Security, Cybersecurity, and cloud infrastructure concepts as they apply to AI system risk.
  • Excellent ability to communicate comfortably with senior management, translating complex AI risk and governance topics into clear, decision‑ready information.
  • Excellent ability to manage a risk profile and design effective mitigation strategies appropriate to AI and agentic system risk scenarios.
  • Working knowledge of NIST AI RMF, OMB AI guidance, FAR/DFARS AI requirements, DoD Responsible AI principles, CMMC implications, EU AI Act, GDPR, UK AI Governance Framework, ISO 42001, OECD AI Principles, and emerging state laws (Colorado, California, Virginia, Texas).
  • Hands‑on familiarity with Microsoft Copilot, Microsoft Purview, OpenAI, Anthropic, Google, and open‑source AI ecosystems; awareness of Palantir, Snowflake, Databricks, ServiceNow.
  • Understanding of Agentic AI 7‑layer operating model, MCP architectures, tool calling, agent‑to‑agent communications, and human approval gates.
  • Excellent analytical, written, and presentation skills; demonstrated ability to produce governance documentation, policy materials, and stakeholder briefings of high quality.
Education and Experience
  • Bachelor's degree or equivalent is required, or the combination of education and relevant work experience, plus a minimum of 8 years of relevant experience; or
  • Master's degree plus a minimum of 6 years of relevant experience in AI governance, technology risk, cybersecurity GRC, responsible AI, or AI/ML compliance.
  • Certifications highly sought include IAPP AI Governance Professional (AIGP), Certified Risk and Information Systems Control (CRISC), Advanced AI Risk (AAIR), ISO 42001.

Target salary range: USD $144,451.00/Yr. - USD $152,000.00/Yr.

Equal Opportunity Employer / Individuals with Disabilities / Protected Veterans

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