Risk Manager II (US)

TD Bank • Southfield, MI

7d • Onsite

About The Position

The Risk Manager II manages the creation, implementation, and validation of various risk segmentation strategies, including but not limited to adjudication, exposure management, risk segmentation, and financial return optimization. The position requires a strong statistical and mathematical background with auto lending experience, expertise in logistic regression, decision trees, clustering, survival analysis, GLM, gradient boosting, scorecard development, reject inference, PD/LGD loss prediction, and machine learning. It also requires hands-on analytics experience with bureau and alternative data sources, and knowledge of feature engineering and attribute aggregation. Advanced proficiency in SAS, R, Python, and related analytical tools is necessary. The position is expected to integrate large, diverse datasets, apply feature engineering, and identify trends and patterns to support model development and enhancement. It will involve developing and enhancing scorecards and models throughout the credit life cycle using statistical methodologies and machine learning techniques. The role will coordinate model deployment across FICO Decision Engine, Python, and related platforms, and partner with stakeholders to support Model Risk Management due diligence and timely issue resolution. Additionally, it will leverage statistical models and vendor data platforms to improve acquisition and portfolio performance, support downstream use of models, consolidate business impact analyses related to model or strategy changes, and communicate key findings to the management team. This role performs functions noted for Risk Manager I, is generally an expert at the enterprise or group business level, acts as the primary regulatory interface on risk issues and requirements for a key business segment of the Bank, and assesses and provides direction for existing and new regulations. It interfaces with teams beyond risk in a cross-functional manner, represents the business on corporate initiatives, identifies key risks and implications, and provides direction in complex situations.

Requirements

• Strong statistical and mathematical background with auto lending experience.
• Expertise in logistic regression, decision trees, clustering, survival analysis, GLM, gradient boosting, scorecard development, reject inference, PD/LGD loss prediction and machine learning.
• Hands-on analytics experience with bureau and alternative data sources, and knowledge of feature engineering and attribute aggregation.
• Advanced proficiency in SAS, R, Python, and related analytical tools.
• Bachelor's degree required.
• 10+ years' experience required.
• Proficient PC skills in MS Office and a variety of PC-based analytical and reporting software packages.
• Experience with the use of Relational Databases and the process of Extract Transform Load (ETL) using common languages such as SQL or SAS.
• Working knowledge of SAS Enterprise Miner, FICO Model Builder or Angoss Knowledge Seeker.
• Strong analytical and problem solving skills are required to interpret data and draw conclusions.
• Flexibility to adapt to rapidly changing requirements.
• Extremely strong attention to detail with ability to manage a range of tasks and prioritize.
• Proven ability to develop and maintain productive business/peer relationships.
• Superb written and verbal communication skills.
• Experienced in developing and presenting recommendations to Senior Management.

Nice To Haves

• Graduate degree preferred or progressive work experience in addition to experience below.

Responsibilities

• Manages the creation, implementation, and validation of various risk segmentation strategies including adjudication, exposure management, risk segmentation, and financial return optimization.
• Integrates large, diverse datasets, applies feature engineering, and identifies trends and patterns to support model development and enhancement.
• Develops and enhances scorecards and models throughout the credit life cycle using statistical methodologies and machine learning techniques.
• Coordinates model deployment across FICO Decision Engine, Python, and related platforms.
• Partners with stakeholders to support Model Risk Management due diligence and timely issue resolution.
• Leverages statistical models and vendor data platforms to improve acquisition and portfolio performance.
• Supports downstream use of models, consolidates business impact analyses related to model or strategy changes, and communicates key findings to the management team.
• Acts as the primary regulatory interface on risk issues and requirements for a key business segment of the Bank and assesses and provides direction for existing and new regulations.
• Represents business on corporate initiatives and identifies key risks and implications and provides direction in complex situations.

Benefits

• Base salary and variable compensation/incentive awards (e.g., eligibility for cash and/or equity incentive awards, generally through participation in an incentive plan)
• Health and well-being benefits
• Savings and retirement programs
• Paid time off (including Vacation PTO, Flex PTO, and Holiday PTO)
• Banking benefits and discounts
• Career development
• Reward and recognition

What This Job Offers

• Job Type: Full-time
• Career Level: Senior