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Model Risk Jobs in Arizona (NOW HIRING)

ClifyX

AI Prompt Engineer

Chandler, AZ · On-site

... Risk, Legal, and Compliance teams to meet regulatory and audit requirements. • Support model governance, SDLC compliance, and production readiness reviews. • Document prompt frameworks, AI ...

Synchrony Financial

VP, Enterprise Risk

Phoenix, AZ · On-site

Maintain RAS governance documentation and controls (e.g., non-model tool inventory/documentation ... Partner across Risk Pillars and the broader business to ensure an aggregated enterprise view of ...

Arkhya Tech

GenAI Solutions Leader

Phoenix, AZ · On-site

Desired Qualifications 5+ years across the AI/ML lifecycle: data management, feature engineering, model development, deployment, monitoring/observability, and model risk/governance. Experience in ...

Early Warning Services

IT Risk Manager

Scottsdale, AZ · Hybrid

Positions located in Scottsdale, San Francisco, Chicago, or New York follow a hybrid work model to ... Overview The Manager, Risk Management is responsible for the building and coordination of a ...

Citizens

Program Manager - AI Engineering

Phoenix, AZ · On-site

$122K/yr

Understanding of model risk management, governance, and compliance within financial services * Demonstrated Executive Partnership & Influence * Supporting senior executives, business leaders, or ...

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All JobsModel Risk JobsModel Risk Jobs in Arizona

Model Risk information

See Arizona salary details

$13

$28

$68

How much do model risk jobs pay per hour?

As of Aug 7, 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 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 Arizona? The most popular types of Model Risk jobs in Arizona are:
What are popular job titles related to Model Risk jobs in Arizona? For Model Risk jobs in Arizona, the most frequently searched job titles are:
What job categories do people searching Model Risk jobs in Arizona look for? The top searched job categories for 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:
Model Risk jobs near you
Infographic showing various Model Risk job openings in Arizona as of August 2026, with employment types broken down into 2% As Needed, 79% Full Time, 14% Part Time, 1% Temporary, and 4% Contract. Highlights an 88% Physical, 4% Hybrid, and 8% Remote job distribution, with an average salary of $58,802 per year, or $28.3 per hour.

AI Platform Director- Data Engineering

First Citizens Bank

Phoenix, AZ

$251K/yr

Full-time

Re-posted 20 days ago

First Citizens Bank rating

7.4

Company rating: 7.4 out of 10

Based on 106 frontline employees who took The Breakroom Quiz

108th of 170 rated banks

Job description

Overview

We are seeking an experienced Director to lead the AI platform engineering and enablement functions within our expanding Cloud Data and AI Platform organization. This role is instrumental in building, operationalizing, and governing the next-generation AI and machine learning ecosystem that powers advanced analytics and responsible AI adoption across the bank. You will own the end-to-end AI lifecycle—from data and model development to MLOps, deployment, governance, and responsible AI compliance in a regulated financial environment.

As a seasoned technology leader, you will bring your expertise in enterprise AI architecture, model operations, and platform engineering to partner with key business, technology, and governance stakeholders—ensuring AI initiatives are responsibly implemented, well-controlled, and deliver measurable value. 


Responsibilities

AWS AI/ML Platform Ownership

  • Architect and lead AI/ML workloads on AWS including:
    • Amazon SageMaker (training, deployment, model registry)
    • AWS Bedrock (foundation models and GenAI use cases)
    • AWS Lambda, ECS, EKS for model serving
    • S3, Glue, Snowflake for data pipelines
  • Define enterprise standards for MLOps, feature stores, and model lifecycle management
  • Build and maintain integrations with enterprise platforms for data ingestion, metadata management, tokenization, and control evidence generation. 
  • Continuously enhance the platform’s automation, resilience, and observability, ensuring robust end-to-end telemetry for both model and data pipelines. 
  • Collaborate with Enterprise Risk, Legal, Compliance, and Model Risk partners to embed Responsible AI principles and audit-ready control evidence directly into platform design. 

Machine Learning & GenAI Execution

  • Oversee development of ML models across all business units including Fraud detection systems, Credit scoring and risk modeling, Customer segmentation and personalization, Liquidity related modeling etc.
  • Lead GenAI initiatives using LLMs for Document intelligence, AI copilots etc.

 

Data & Engineering Collaboration

  • Partner with data engineering teams to ensure high-quality, governed datasets
  • Define feature engineering and data product standards in Snowflake / data lake environments
  • Integrate real-time streaming data for low-latency decision systems

 

Model Governance & Risk Compliance

  • Define and enforce standards, patterns, and guardrails for model deployment, explainability, lineage, and monitoring in alignment with enterprise risk, compliance, and security frameworks. 
  • Partner closely with leaders across Responsible AI Governance, AI Portfolio Management, AI Fluency & Engagement, and Applied Data Science & GenAI, in collaboration with enterprise risk partners, to implement a responsible AI framework that embeds audit-ready control evidence and governance mechanisms directly into the platform’s core design to ensure the platform supports scalable, ethical, compliant, and high-impact AI delivery. 
  • Implement model explainability (SHAP, LIME, interpretability frameworks)
  • Establish responsible AI policies (bias detection, fairness, auditability)

 

Team Building & Leadership

  • Develop and mentor engineering talent, championing Agile practices, continuous learning, and adoption of emerging AI and data engineering technologies. 
  • Mentor senior technical leaders and establish engineering best practices
  • Oversee technical due diligence, onboarding, and management of strategic AI and GenAI vendors and tools, ensuring compatibility with enterprise architecture and control

Qualifications

Bachelor's Degree and 8 years of experience in Information Technology including application development, support roles, and management. OR High School Diploma or GED and 12 years of experience in Information Technology including application development, support roles, and management.

Qualifications

  • Deep hands-on experience building production ML systems on AWS
  • 2+ years in AI/ML, data science, or data engineering leadership roles
  • Strong knowledge of:
    • Machine learning (XGBoost, deep learning, NLP, time series)
    • MLOps practices (CI/CD, model monitoring, drift detection)
    • Distributed systems and cloud architecture
  • Strong programming background in Python + SQL (Scala/Java a plus)
  • Experience working in regulated environments with model governance

Preferred Qualifications

  • Experience with Generative AI / LLM platforms (Bedrock, OpenAI, Claude APIs)
  • Experience in financial services, banking, fintech, or insurance
  • Familiarity with data platforms like Snowflake, Databricks

#LI-JM1

Benefits are an integral part of total rewards and First Citizens Bank is committed to providing a competitive, thoughtfully designed and quality benefits program to meet the needs of our associates. More information can be found at https://jobs.firstcitizens.com/benefits.

Qualifications:

Bachelor's Degree and 8 years of experience in Information Technology including application development, support roles, and management. OR High School Diploma or GED and 12 years of experience in Information Technology including application development, support roles, and management.

Qualifications

  • Deep hands-on experience building production ML systems on AWS
  • 2+ years in AI/ML, data science, or data engineering leadership roles
  • Strong knowledge of:
    • Machine learning (XGBoost, deep learning, NLP, time series)
    • MLOps practices (CI/CD, model monitoring, drift detection)
    • Distributed systems and cloud architecture
  • Strong programming background in Python + SQL (Scala/Java a plus)
  • Experience working in regulated environments with model governance

Preferred Qualifications

  • Experience with Generative AI / LLM platforms (Bedrock, OpenAI, Claude APIs)
  • Experience in financial services, banking, fintech, or insurance
  • Familiarity with data platforms like Snowflake, Databricks

#LI-JM1

Benefits are an integral part of total rewards and First Citizens Bank is committed to providing a competitive, thoughtfully designed and quality benefits program to meet the needs of our associates. More information can be found at https://jobs.firstcitizens.com/benefits.

Education:UNAVAILABLEEmployment Type: FULL_TIME

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