Data Scientist III - Financial Crimes Modeling

Work Location: New York, New York, United States of America

This role is not eligible for TD work visa support or sponsorship. Applicants must have authorization to work in the United States without current or future need for TD sponsorship.

About the Role

The Data Scientist III provides technical leadership across the overall Analytics function which may have an enterprise mandate. This role generally provides deep technical knowledge and expertise in client interactions to explain complex data analysis related material.

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 a Data Science specialist 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.

Responsibilities

• Works closely with business owners to identify opportunities and serves as an ambassador for data science.
• Translates business problems into viable data science solutions using familiarity with business context and data infrastructure.
• Uses programming languages (e.g., Python) and techniques for data extraction, preparation, statistics, and advanced analytics to extract insights from big data.
• Visualizes insights to tell and illustrate data-driven stories for decision-makers and stakeholders at all technical levels.
• Collaborates with data analysts, software engineers, data engineers, and application developers to develop scalable, sustainable data science solutions.
• Provides analytical thought leadership and stays current on developments in data mining and data science applications.
• Improves business processes through insights to enhance effectiveness and efficiency.
• Educates the organization on hypothesis testing and statistical validation of results.
• Helps the organization understand the principles and math behind data science processes to drive alignment.
• Translates up-to-date information into continuous improvement activities that enhance performance.
• Adheres to enterprise frameworks and methodologies for business area activities.
• Ensures programs, policies, and practices meet business needs and comply with internal/external requirements.
• Participates in cross-functional initiatives as a subject matter expert to identify risks and provide guidance.
• Monitors service, productivity, and efficiency, implementing improvements where opportunities exist.
• Leads or facilitates action/remediation plans for performance, risk, or governance issues.
• Manages relationships within and across business lines, corporate, and control functions to ensure alignment with enterprise and regulatory requirements.
• Keeps abreast of emerging issues, trends, and evolving regulatory requirements, assessing potential impacts.
• Maintains a culture of risk management and control aligned with risk appetite.
• Participates fully as a team member, supporting a positive work environment that promotes service, quality, innovation, and teamwork.
• Provides thought leadership and industry knowledge, participating in knowledge transfer within the team.
• Keeps current on emerging trends and developments, growing knowledge of the business and related tools.
• Participates in personal performance management, development activities, and cross-training.
• Keeps others informed about project status and relevant information.
• Contributes to team development through mentorship, sharing knowledge, and leveraging best practices.
• Leads, motivates, and develops relationships with internal and external partners to foster productive working relationships.
• Contributes to a fair, positive, and equitable environment that supports diversity.
• Acts as a brand ambassador for the business area and the bank.

Requirements

• Undergraduate degree or advanced technical degree preferred (e.g., math, physics, engineering, finance, or computer science).
• Graduate degree preferred with either progressive project work experience or 5+ years of relevant experience; higher degree education and research tenure can be counted.
• In lieu of 5+ years of relevant experience, 3+ years of relevant experience will be considered.
• Proficient with Python or equivalent programming language and SQL.
• Experience in Financial Crimes / Compliance Risk Analytics is a plus.
• Experience in optimization, testing, or tuning of AML/CTF solutions, particularly transaction monitoring systems.
• Experience in machine learning model development, validation, and deployment; experience with LLM-based solutions a plus.
• Familiarity with cloud platforms (Azure) and modern data stack tools a plus.

Pay

$96,130 - $155,950 USD

TD is committed to providing fair and equitable compensation opportunities. The base pay offered may vary based on skills, experience, job-related knowledge, geographic location, and other business needs.

Schedule

40 hours per week.

Benefits

Our Total Rewards package includes:

• Base salary and variable compensation/incentive awards (e.g., cash and/or equity incentive awards).
• 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 and reward/recognition programs.

We provide training and onboarding sessions, regular development conversations, and access to an online learning platform and mentoring programs to support your growth.

Physical Requirements

• Domestic Travel: Occasional (1-33%)
• International Travel: Never (0%)
• Performing sedentary work: Continuous (67-100%)
• Performing multiple tasks: Continuous (67-100%)
• Operating standard office equipment: Continuous (67-100%)
• Responding quickly to sounds: Occasional (1-33%)
• Sitting: Continuous (67-100%)
• Standing: Occasional (1-33%)
• Walking: Occasional (1-33%)
• Moving safely in confined spaces: Occasional (1-33%)
• Lifting/Carrying (under 25 lbs.): Occasional (1-33%)
• Lifting/Carrying (over 25 lbs.): Never (0%)
• Squatting: Occasional (1-33%)
• Bending: Occasional (1-33%)
• Kneeling: Never (0%)
• Crawling: Never (0%)
• Climbing: Never (0%)
• Reaching overhead: Never (0%)
• Reaching forward: Occasional (1-33%)
• Pushing: Never (0%)
• Pulling: Never (0%)
• Twisting: Never (0%)
• Concentrating for long periods: Continuous (67-100%)
• Applying common sense to standardized problems: Continuous (67-100%)
• Reading, writing, and comprehending instructions: Continuous (67-100%)
• Adding, subtracting, multiplying, and dividing: Continuous (67-100%)