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Model Risk Manager Jobs in Centralia, WA (NOW HIRING)

Lead AI Quality Assurance Engineer

Olympia, WA Β· Hybrid

$138K/yr

... model performance, automation workflows and user outcomes. Works with engineering, data science, product management, cybersecurity, legal and risk teams to validate AI-enabled products and ...

... risk management issues are following company standards. * Strong business acumen and ability to ... Inspires trust, models best practices, and cultivates morale and teamwork amongst team members.

Case Manager I

Olympia, WA Β· On-site

$25.46 - $31.83/hr

... model for chronically homeless and disabled adults. The program will have 24-hour monitoring staff ... risk, etc. * Assist clients with transportation or transportation resourcesto medical, mental ...

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Showing results 1-20

Model Risk Manager information

See Centralia, WA salary details

$54K

$116.9K

$178.2K

How much do model risk manager jobs pay per year?

As of Sep 13, 2026, the average yearly pay for model risk manager in Centralia, WA is $116,940.00, according to ZipRecruiter salary data. Most workers in this role earn between $94,300.00 and $135,200.00 per year, depending on experience, location, and employer.

What does a model risk manager do?

A Model Risk Manager is responsible for identifying, assessing, and mitigating risks associated with financial and analytical models used by an organization. They ensure that models are accurate, reliable, and compliant with regulatory standards by overseeing validation processes and monitoring model performance. Their role often includes collaborating with model developers, conducting independent reviews, and implementing model governance frameworks to minimize potential losses or errors stemming from model misuse or inaccuracies.

What skills and qualifications are needed to be a model risk manager?

To thrive as a Model Risk Manager, you need a solid background in quantitative finance, statistics, or mathematics, often supported by an advanced degree and experience in model development or validation. Familiarity with programming languages such as Python or R, risk management frameworks, and regulatory requirements like SR 11-7 or ECB guidelines is typically expected. Strong analytical thinking, attention to detail, and effective communication are crucial soft skills for articulating complex model risks to stakeholders. These competencies are vital for ensuring the accuracy, compliance, and reliability of financial models within an organization.

What are common challenges a model risk manager faces when validating complex financial models?

Model Risk Managers often encounter challenges such as limited or incomplete data, evolving regulatory requirements, and the need to validate highly complex or proprietary models. They must work closely with model developers, quantitative analysts, and compliance teams to ensure all assumptions and methodologies are sound. Staying up to date with industry best practices and maintaining clear documentation are also crucial, as is effectively communicating findings to both technical and non-technical stakeholders.

What is the difference between Model Risk Manager vs Quantitative Analyst?

AspectModel Risk ManagerQuantitative Analyst
Required CredentialsAdvanced degrees in finance, statistics, or mathematics; certifications like FRM or CFADegree in finance, economics, mathematics, or related fields; often CFA or CQF
Work EnvironmentFocus on risk management teams within financial institutions; regulatory complianceAnalytical roles within trading, investment, or banking divisions; model development
Employer & Industry UsageFinancial institutions, banks, asset managersInvestment firms, hedge funds, banks, financial services

The Model Risk Manager primarily oversees and mitigates risks associated with financial models, ensuring compliance and accuracy. In contrast, Quantitative Analysts develop and implement models to support trading, investment, or risk strategies. While both roles require strong quantitative skills and similar credentials, their focus areas differβ€”risk management versus model development and analysis.

Infographic showing various Model Risk Manager job openings in Centralia, WA as of July 2026, with employment types broken down into 83% Full Time, 16% Part Time, and 1% Contract. Highlights an 82% Physical, 2% Hybrid, and 16% Remote job distribution, with an average salary of $116,940 per year, or $56.2 per hour.

Fraud & Risk Manager (WMS2)

Olympia, WA β€’ On-site

Other

Medical, Dental, Vision, Life, Retirement

Posted 11 days ago


Key responsibilities

  • Lead the agency's response to fraud risk events.

  • Own and maintain the enterprise fraud risk register.

  • Provide fraud training and guidance.


Job description

Description

The hiring manager will begin reviewing applications after seven days and will begin interviewing and may make a hiring decision at any time after that. It is in the candidates’ best interest to apply early.

The Department of Revenue (Revenue) Executive division is seeking a dynamic professional with extensive fraud management, risk management, audit, or internal control design/assessment experience to serve as the Fraud & Risk Manager. This position supports the agency-wide Enterprise Risk Management and Internal Control programs. The incumbent is primarily responsible for designing and overseeing a coordinated enterprise framework and approach for fraud detection, assessment, prevention, investigation, and mitigation strategies to minimize fraud risk exposure to Revenue.

This position serves agency executives and leadership in an oversight and advisory capacity and requires a high degree of collaboration and self-motivation. As a fraud subject matter expert, the role partners with agency divisions to help assess fraud risks associated with agency-wide practices and to support proactive control improvements.

The Fraud & Risk Manager will work to build relationships with Department fraud managers, and with fraud managers in other Washington state agencies to evolve a community of practice. In addition, this position will develop contacts with fraud specialists at other state revenue agencies to discuss emerging risks and strategies to prevent successful fraud attacks.

Revenue is a dynamic learning organization where you will experience a remarkable work-life balance, with amazing leadership and talented co-workers ready and focused to achieve the agency's goals. We value diverse perspectives and life experiences. We employ and serve people of all backgrounds including people of color, immigrants, refugees, LGBTQ+, people with disabilities, and veterans. This unique culture of respect promotes a professional family of cohesive groups maximizing potential through opportunity. We offer a generous benefits package that includes defined benefit retirement plans; health, dental and vision coverage, deferred compensation plans, and as a public service employee, you may also be eligible for student loan forgiveness.

Duties

The position reports directly to the Enterprise Risk Officer and will be responsible to provide extensive subject matter expertise in process/root cause analysis, identification of fraud risks, risk assessment methods/techniques, and fraud internal control analysis and design. As the Fraud & Risk Manager, you will be responsible to:

  • Progress the agency’s fraud mitigation maturity, as guided by the established Enterprise Fraud Framework and related fraud maturity model.
  • Own and maintain the enterprise fraud risk register in line with the agency’s Enterprise Risk Framework.
  • Assess Fraud Mitigation Effectiveness.
  • Lead the agency’s response to fraud risk events.
  • Provide fraud training and guidance.
Qualifications

Please ensure that your application materials address the following qualifications~

We are looking for candidates who possess the following core competencies:

Required Education, Experience, and Competencies.

Core Competencies
  1. Progress the Agency’s Fraud Mitigation Maturity
    • Fraud Risk Management Knowledge
    • Understanding of fraud risk management principles, maturity models, and the COSO frameworks.
    • Ability to integrate fraud mitigation with enterprise risk and control systems.
  2. Own and Maintain the Enterprise Fraud Risk Register
    • Risk Identification & Assessment
    • Skill in conducting structured fraud risk assessments, including identifying vulnerabilities and exposure.
    • Understanding of risk scoring methodologies.
    • Ability to classify and describe mitigating controls (e.g. detective, preventive, IT, automated, manual).
    • Root Cause Analysis
    • Ability to identify systemic issues and control weaknesses contributing to fraud risks.
    • Competence in recommending targeted remediation strategies.
  3. Assess Fraud Mitigation Effectiveness
    • Control Design & Evaluation
    • Ability to assess adequacy of existing internal controls and help design effective preventive and detective controls.
    • Proficiency in applying internal control frameworks to assess control maturity, identify gaps, and make balanced recommendations to strengthen fraud mitigations in alignment with organizational risk appetite.
  4. Lead the agency’s response to fraud risk events
    • Crisis Management & Incident Response
    • Ability to design and operationalize incident response plans for fraud events.
    • Skill in defining escalation procedures and decision-making protocols during incidents.
    • Skill in exploring alternative attack paths to anticipate potential threats and evolving fraud tactics.
    • Ability to identify vulnerabilities and weaknesses in agency controls and prevent future incidents.
  5. Provide Fraud Training and Guidance
    • Training Development & Facilitation
    • Ability to design and deliver fraud awareness and prevention training tailored to various audiences.
    • Skill in instructional design, adult learning principles, and facilitation of group discussions.
    • Fraud Awareness & Prevention Knowledge
    • Strong knowledge of common fraud schemes, red flags, and investigative standards.
    • Understanding of ethical considerations and equitable treatment in fraud prevention.
  6. General / Foundational Competencies
    • Integrity & Ethical Judgment
    • High ethical standards and confidentiality in managing sensitive fraud-related data.
    • Data Visualization & Dashboarding
    • Ability to design dashboards and fraud monitoring tools which translate complex data into executive-ready visuals.
    • Proficiency with analytics tools or visualization platforms (e.g., Power BI, Tableau, or Excel dashboards).
    • Project Management
    • Ability to manage multiple initiatives, deadlines, and stakeholder expectations effectively.
    • Stakeholder Engagement & Collaboration
    • Skill in coordinating across divisions, programs, and leadership levels to align fraud mitigation objectives.
    • Ability to build consensus and drive action among diverse stakeholders.

Diversity, Equity & Inclusion Awareness Sensitivity to how fraud controls and detection strategies may impact marginalized populations.

Ability to ensure balanced approaches that protect program integrity and fairness.

The ability to take action to learn and grow.

The ability to take action to meet the needs of others.

Actively seeks to understand and appreciate the diverse backgrounds, perspectives, and experiences of colleagues, customers, and communities.

A bachelor’s degree in accounting, finance, business/public administration, criminal justice, data analytics or related field.

One or more of the following certifications:

  • Certified Fraud Examiner (CFE) certification.
  • Certification in risk management.
  • Lean Six Sigma Green Belt certification or similar.
  • Certified Internal Auditor

Experience with the following:

  • At least 5 years of experience in fraud risk management.
  • Experience developing fraud mitigation programs.
  • Experience working in a public sector or regulatory environment.
  • Experience developing and managing fraud risk registers or similar tools.
  • Experience conducting fraud assessments, assessing controls and maturity model analysis.
  • Familiarity with data analytics and visualization tools for fraud detection or reporting (e.g., Power BI, Tableau, Excel).
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