AI Model Risk Analyst / Algorithm Compliance Specialist

Role Description The AI Model Risk Analyst / Algorithm Compliance Specialist is responsible for ensuring that artificial intelligence and machine learning models are developed, deployed, and monitored in a safe, compliant, and explainable manner. The role focuses on identifying, assessing, and mitigating risks associated with AI models, including bias, drift, data integrity issues, and regulatory non-compliance.

Key responsibilities include validating AI/ML models before deployment, conducting ongoing performance and stability monitoring, and ensuring adherence to internal governance frameworks and external regulatory standards. The role works closely with data science, engineering, risk, legal, and compliance teams to ensure models meet ethical and operational requirements.

The analyst will also design and implement model risk controls, documentation standards, and audit processes. This includes evaluating model assumptions, testing outputs for fairness and robustness, and preparing compliance reports for internal and external audits.

In addition, the role supports the development of AI governance policies, contributes to model lifecycle management, and helps establish best practices for responsible AI usage across the organization.

Qualifications

• Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Finance, or a related field

• 2–6 years of experience in model risk management, data science, AI/ML development, or compliance/risk roles

• Strong understanding of machine learning algorithms, statistical modeling, and model validation techniques

• Knowledge of model risk frameworks and governance standards (e.g., SR 11-7 or equivalent) is an advantage

• Experience with programming languages such as Python or R for model evaluation and analysis

• Familiarity with AI/ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch) is a plus

• Strong analytical and critical thinking skills, especially in identifying model bias, drift, and anomalies

• Understanding of data governance, privacy, and regulatory compliance requirements

• Experience with explainable AI (XAI) techniques and model interpretability tools is preferred

• Ability to document complex technical findings clearly for audit and compliance purposes

• Strong communication skills to collaborate across technical, risk, and business teams

• Certifications in risk management, AI governance, or data science are a plus but not required