Analyst, Model Risk Management

Toast is driven by building the restaurant platform that helps restaurants adapt, take control, and get back to what they do best: building the businesses they love. Now, more than ever, the Toast team is committed to our customers. We’re taking steps to help restaurants navigate these unprecedented times with technology, resources, and community. Our focus is on building the restaurant platform that helps restaurants adapt, take control, and get back to what they do best: building the businesses they love. And because our technology is purpose-built for restaurants, by restaurant people, restaurants can trust that we’ll deliver on their needs for today while investing in experiences that will power their restaurant of the future. 

Bready* to make a change?

As an Analyst in our Model Risk Management team, you will help manage the Model Risk Program including completing model validation reviews, maintaining the model risk inventory, and monitoring model performance. You will work closely with the Data Science Team, architects, engineers and product managers to assess the risk of model design, implementation, and use across product lines.

About this Roll*: (Responsibilities)

• Support the implementation and day-to-day operations of the second line of defense model risk program for high-risk models at Toast.
• Assist in maintaining model risk management program documentation, including frameworks, policies, procedures, templates, and best practices.
• Help enforce key model requirements and standards across model development, implementation, use, validation, change management, governance, and controls.
• Contribute to the development and maintenance of a comprehensive model risk inventory, including updating risk information for each model.
• Perform model validation reviews under the guidance of the Senior Manager, draft validation reports, and track the progress of resulting remediation plans.
• Develop and implement model performance monitoring frameworks to track and report model deviations from expected behavior.
• Collaborate with cross-functional teams (Data Science, Data Engineering, Information Security, Product Engineering, Finance, Credit Risk, and Business Strategy) to collect data, provide effective challenge, support model validation efforts, and oversee ongoing monitoring activities.
• Prepare draft reports and materials on model risk management issues, risks, and practices for leadership review. Assist in compiling information for risk management committees, audit committee of the board, and internal audit reports.
• Research and stay informed about industry trends, emerging data science methodologies, and evolving model risk practices. Propose ideas for enhancing Toast's model risk management program.
• Contribute to the development of model risk training materials and assist in delivering training sessions to relevant stakeholders.
• Support ad-hoc projects related to model risk management and regulatory compliance initiatives.

Do you have the right ingredients*? (Requirements)

• Advanced degree in Data Science, Statistics, Applied Math, Computer Science, Engineering or other equivalent quantitative disciplines
• 2-5  years of industry experience in Model Risk Management and / or Data Science and Machine Learning at a fintech, bank, technology company, or consulting firm with a focus on retail lending and credit risk.
• Proficiency in machine learning and mathematical foundations of statistics.
• Strong proficiency in Python and SQL.
• Familiarity with data science libraries and tools such as Spark, Scala, scikit-learn, TensorFlow, PyTorch, XGBoost etc., and/or standard software engineering practices and tools including object-oriented programming, test-driven development, CI/CD, git, task orchestration (Airflow), and AWS tooling is a plus
• Knowledge and understanding of model risk management frameworks, practices, and regulatory guidelines (e.g., SR 11-7, OCC 2011-12).
• Ability to comprehend business problems, effectively challenge quantitative methodologies and articulate complex information to both technical and non-technical stakeholders.
• Strong critical and analytical thinking, innovative problem-solving abilities, demonstrated ability to meet deadlines with high-quality deliverables, and excellent verbal and written communication skills.