Data Scientist II (US) FCRM Modeling

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.

Eligibility: Applicants must be either a US Citizen or have a US Resident Green Card authorization, without current or future need for TD sponsorship . This role is not eligible for TD work visa support or sponsorship (e.g., H-1B, F-1 OPT/STEM OPT, TN or  other work visa authorizations).

The Data Scientist II is responsible for collecting data and using wide range of data science techniques, including but not limited to data wrangling, profiling and visualization, statistical inference, to uncover actionable insights or build analytics solutions that guide decision making and strategic planning. Department Overview: The US Financial Crime Risk Modeling & Advanced Analytics team within US Financial Crime department is responsible for developing, maintaining, and enhancing the Enterprise Anti-Money Laundering / Counter-Terrorism Financing (AML/CTF) models/AI solutions to comply with regulatory requirements/changes and internal policies, support TD's global AML/CTF strategies, address emerging risks, and be in accordance with best industry practice. We are seeking data scientists at various levels to join us to innovate, drive, and support initiatives and business as usual operations in multiple functional areas including, but not limited to, customer rating, sanctions screening, transaction monitoring, emerging risk, model performance monitoring, analytics and reporting. Depth & Scope: Works autonomously within a specialized business management function and may provide work direction to others Provides seasoned specialized knowledge, advice and/or guidance to various stakeholders and team members Scope of role may have enterprise impact Focuses on short to medium - term issues (e.g. 6-12 months) Undertakes and completes a variety of complex projects and initiatives requiring specialist knowledge and/or the integration of cross functional processes within own area of expertise Oversees and/or independently performs tasks from end-to-end Education & Experience: Undergraduate degree or advanced technical degree preferred (e.g., math, physics, engineering, finance or computer science) Graduate's degree preferred with either progressive project work experience, or; 3+ year of relevant experience; higher degree education and research tenure can be counted Preferred Skills: Graduate degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related quantitative discipline; academic research experience may be considered equivalent to industry experience Proficiency in Python and SQL coding Hands-on experience with advanced quantitative analysis, statistical modeling, and machine learning Strong communication skills, with the ability to present analytical findings to both technical and non-technical audiences Experience with LLM prompt engineering and performance evaluation is a plus Knowledge of financial crime, anti-money laundering (AML), sanctions screening, watchlist screening, or compliance risk analytics is a plus Awareness of Responsible AI and model governance is a plus Customer