Senior Manager, Data Science (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.

The Senior Manager, Data Science leads a specialized team of data professionals varying in size and complexity that are responsible for aiding to drive changes and improvement in business practices through data science. This role manages the overall data scientist team or function for a key business which may include Modelers and/or Data Scientist roles. This role may also oversee the development of data models, data mining and analytic solutions. Day to Day: The Senior Manager, Data Science will deliver measurable value through predictive, prescriptive, and generative AI capabilities. The successful candidate will combine deep technical expertise with strong leadership skills to build high-performing teams and establish a culture of innovation, experimentation, and responsible AI. Lead and develop a team of Data Scientists, providing coaching, mentorship, technical guidance, and career development. Partner with business leaders to identify and prioritize high-value machine learning, artificial intelligence, and advanced analytics opportunities. Translate complex business problems into analytical frameworks, models, and actionable insights. Oversee the end-to-end data science lifecycle, including problem formulation, feature engineering, model development, validation, deployment, monitoring, and ongoing optimization. Guide the development of predictive, prescriptive, and generative AI solutions that improve business performance, efficiency, customer experience, and risk management outcomes. Collaborate with data engineering, technology, and platform teams to ensure scalable and production-ready solutions. Establish model governance, validation, explainability, documentation, and monitoring practices in accordance with enterprise risk management standards. Evaluate emerging AI, machine learning, and data science techniques and recommend practical applications across the organization. Present analytical findings, recommendations, and business cases to senior executives and stakeholders in a clear and impactful manner. Drive experimentation and innovation through proof-of-concepts, pilots, and test-and-learn initiatives. Ensure responsible and ethical use of AI through adherence to regulatory requirements, governance frameworks, and internal policies. Manage portfolio planning, resource allocation, and delivery execution across multiple concurrent data science initiatives. Depth & Scope: Provides people management leadership by hiring the best talent, setting goals, developing staff, managing employee performance and compensation decisions, promoting teamwork and handling any/all disciplinary actions, as required Oversees and leads a large and/or highly complex and diverse analytical function for an area of significant risk, complexity or scope Strategic partner to leadership team on the management of the portfolio and financials, with deep industry, external/internal, enterprise knowledge, recognizing and anticipating emerging trends and identifying operational efficiencies and opportunities with other business management/enterprise