Audit Manager I (US)

TD is committed to providing fair and equitable compensation opportunities to all colleagues. Growth opportunities and skill development are defining features of the colleague experience at TD. Our compensation policies and practices have been designed to allow colleagues to progress through the salary range over time as they progress in their role. The base pay actually offered may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational needs.  As a candidate, you are encouraged to ask compensation related questions and have an open dialogue with your recruiter who can provide you more specific details for this role.

Job Description: The Audit Manager I executes audits for an assigned business, function or project as part of a team or as an individual contributor and/or provide subject matter expertise on audits ranging in complexity. May manage/lead a number of moderately complex audits, related engagement and/or projects/initiatives. and has responsibility for completion of the audit.   Depth & Scope:   Works as an audit subject matter expert and may coach and educate others   Oversees and/or independently performs audits from end-to-end   May lead moderately complex audits and have responsibility for completion of the audit   Undertakes and completes a variety of projects and initiatives, may include the integration of cross functional processes within own area of expertise   Ability to process and handle confidential information with discretion   Strong understanding of and practical experience with quantitative financial models, including Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD), and other credit loss models; discounted cash flow and derivatives pricing models; Treasury non-trading market risk models; counterparty credit risk models; Value-at-Risk (VaR) models; portfolio risk and performance models; as well as Financial Crime Risk (AML/BSA) and fraud detection models. Demonstrated ability to assess model design, development, validation, implementation, performance monitoring, and governance across these model types.   Strong knowledge of statistical and quantitative techniques, including regression analysis, hypothesis testing, probability theory, time series analysis, stochastic processes, simulation methodologies, model performance measurement, and model monitoring. Demonstrated experience in assessing AI-related risks, with practical expertise in reviewing and evaluating Machine Learning (ML), Generative AI (GenAI), and/or Agentic AI models, including their development, validation, governance, and ongoing performance monitoring.   AML/BSA model experience is preferred   but not strictly required,   including familiarity with transaction monitoring, alert triage, customer risk rating, and screening models.   E xperience evaluating model assumptions, data quality, feature engineering, model limitations, performance, governance, explainability, fairness, robustness, and ongoing monitoring across traditional statistical, machine learning, and AI models.    Proficiency in one or more programming languages or analytical platforms, such as Python, SQL, SAS, R, MATLAB, VBA, or other quantitative and data analytics technologies.    Experience with model implementation, deployment, lifecycle management, and production model environments is considered an asset.    Knowledge of model risk management frameworks, regulatory expectations, and industry standards, including SR 26-2, Basel, CCAR, CECL/IFRS 9, and AI governance frameworks, is preferred.    Prior experience conducting model audits, model validations, or independent model reviews within financial institutions, regulatory organizations, or consulting firms is highly