Model Risk Analyst

As a Model Risk Analyst, your primary responsibilities will include validating models, conducting audits, and ensuring they meet performance standards. You'll generate governance and performance statistics and help create new governance standards for the company. Additionally, you'll serve as the main point of contact for governance materials, assisting in deep model audits with major financial institutions.

To apply for the Model Risk Analyst position, candidates should have at least 3 years of experience in a relevant field and hold a technical bachelor's degree in any STEM discipline. Proficiency in Python at an intermediate level and strong skills in Excel or Google Sheets are essential, along with experience in data visualization. Candidates must also be legally authorized to work in the U.S. and reside within the country.

The ideal candidate for the Model Risk Analyst role will have experience in model governance, risk management, and backtesting. Familiarity with tools such as GitHub and the AWS ecosystem, including S3 and SageMaker, will also be advantageous. Most importantly, a knack for collaboration and communication with stakeholders is vital for success in this remote role.

A Model Risk Analyst should be well-versed in Python for intermediate data analysis and have strong Excel or Google Sheets skills. Experience with data visualization tools like BI tools or Excel plots is important. Additionally, to enhance efficiency and quality in governance documentation, familiarity with GitHub and AWS tools such as S3 and SageMaker is highly desirable.

The salary range for the Model Risk Analyst position is between $110,000 and $140,000 per year, complemented by equity and a comprehensive benefits package. This reflects our commitment to attracting and retaining top talent in the industry.

In validating a model, I begin by reviewing its application and theoretical foundations to ensure clarity. Then, I perform statistical tests to check its accuracy and reliability, followed by backtesting it against historical data. Additionally, I gather input from relevant stakeholders and document any findings for transparency.

Assessing model risk revolves around identifying potential areas where inaccuracies may arise. I analyze the assumptions behind a model, examine the data quality, and evaluate the impact of model performance on business decisions. Furthermore, I ensure alignment with regulatory standards to mitigate compliance risks.

In one instance, while reviewing a predictive model, I discovered a critical assumption that was based on outdated market conditions. I flagged this for further review, and suggested adjustments that ultimately improved the model’s accuracy and safeguarded the organization against potential financial loss.

I typically rely on several key metrics, including R-squared to evaluate goodness of fit, Mean Absolute Error (MAE) for predictive accuracy, and stability measures to ensure the model is resilient over time. These metrics collectively inform my assessment of model performance and their effectiveness.

When communicating model risks to non-technical stakeholders, I focus on clarity and relevance. I utilize visuals and simple language to illustrate concepts while maintaining technical integrity, making it easier for stakeholders to understand implications and necessary actions for risk mitigation.

I have extensive experience using data visualization tools like Tableau and Power BI to present complex data insights in an accessible manner. I believe data visualization is crucial for effective communication of model performance and risk analysis to both technical and non-technical audiences.

Ensuring compliance with model governance standards involves staying updated on regulations and best practices, conducting regular audits, and engaging with internal stakeholders for feedback. Additionally, I maintain comprehensive documentation that reflects adherence to these standards throughout the model lifecycle.

To enhance governance documentation, I advocate for simplification and standardization of templates, fostering a consistent format that makes documentation easier to navigate. I also solicit feedback from users to continuously refine documents based on practical experiences.

In model governance, I use GitHub for version control and collaboration on governance documents and code. This facilitates tracking changes, coordinating feedback from team members, and maintaining an organized repository, ensuring everyone is aligned on model revisions and enhancements.

I regularly engage in continuous learning through industry publications, webinars, and conferences focused on model risk management. Networking with professionals in the field and participating in online forums also allows me to stay informed of new developments and emerging best practices.