Model Risk Management Engineer

Job Title : Model Risk Management Engineer

Job Summary :

We are looking for a talented Model Risk Management Engineer to join our team. The ideal candidate will possess strong expertise in model testing, automation, and quantitative analysis. This role requires hands-on experience with AWS cloud services, Python development, and a deep understanding of software testing methodologies as they relate to model validation. You will be responsible for designing and implementing automated test frameworks, validating complex quantitative models, performing functional and regression testing, and ensuring the accuracy, reliability, and regulatory compliance of analytical systems.

Key Responsibilities :

Model Validation & Risk Assessment :

Perform in-depth testing and validation of complex quantitative models used across the organization.

Identify and assess model risk, ensuring the integrity, reliability, and performance of models.

Develop and implement procedures for validating the effectiveness of quantitative models.

Automation & Testing Frameworks :

Design and implement automated test frameworks for model validation, ensuring comprehensive coverage and efficiency.

Conduct functional, regression, and performance testing of models and analytical systems.

Automate repetitive testing tasks to increase efficiency and reduce human error.

Collaboration & Documentation :

Work closely with quantitative analysts, data scientists, and software engineers to align testing strategies with business needs.

Develop clear and concise test plans, test cases, and documentation to track model performance, risk assessment, and validation results.

Ensure all testing and validation procedures comply with internal standards and external regulations.

Cloud Infrastructure & Data Analysis :

Utilize AWS cloud services for the deployment, monitoring, and testing of models in cloud environments.

Leverage Python for developing and maintaining test scripts, data pipelines, and model validation processes.

Analyze model results and provide actionable insights to ensure optimal model performance.

Continuous Improvement :

Identify and implement process improvements in model validation and risk management practices.

Stay up-to-date with industry trends, regulatory changes, and emerging technologies to continuously enhance testing methodologies.

Key Requirements :

Education & Experience :

Bachelor's or Master's degree in a quantitative field (e.g., Computer Science, Engineering, Mathematics, Finance, or similar).

3+ years of experience in model risk management, model validation, or quantitative engineering roles, preferably in financial services or similar industries.

Hands-on experience with AWS cloud services and related tools for cloud-based model testing and deployment.

Technical Skills :

Proficient in Python programming, particularly for automation, testing, and data analysis.

Experience with model risk management frameworks, testing methodologies, and model validation techniques.

Strong knowledge of software testing concepts (unit testing, integration testing, regression testing) and their application to quantitative models.

Analytical & Problem-Solving Skills :

Strong analytical skills, with the ability to identify issues, propose solutions, and recommend improvements in testing and model validation processes.

Experience in using tools like AWS S3, Lambda, EC2, and other cloud-native services for model validation.

Communication & Collaboration :

Strong written and verbal communication skills for documenting testing processes, results, and risk assessments.

Ability to collaborate with cross-functional teams to understand model requirements and ensure alignment with organizational goals.

Preferred Qualifications :

Familiarity with financial modeling, risk modeling, or quantitative finance.

Knowledge of machine learning or AI techniques and their validation methodologies.

Experience in regulatory compliance related to model risk management (e.g., SR 11-7, Basel requirements).