Data Scientist - FCRM

Work Location:

Charlotte, North Carolina, United States of America

Hours:

40

Pay Details:

$76,280 - $125,260 USD

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.

Line of Business:

Analytics, Insights, & Artificial Intelligence

Job Description:

Please be aware that this role within this line of business is only eligible to those candidates that are U.S. Citizens / Green Card Holders, and will not eligible for TD work visa support or sponsorship (e.g., H-1B, F-1 OPT/STEM OPT, TN or other work visa authorizations).  Applicants must have authorization to work in the United States without current or future need for TD sponsorship.

Job Description:

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:

This position will be responsible for creating, developing, and maintaining a range of AML typologies, leveraging analytics to proactively identify and mitigate financial crime risks. The role involves close collaboration with cross-functional teams, supporting Financial Intelligence Units (FIU) through targeted data analysis, and contributing to strategic initiatives that strengthen our AML efforts across the organization. The individual will oversee the successful execution of multiple projects, ensuring they are completed within established timelines and providing a second level of oversight throughout. Prior experience and expertise with machine learning, Microsoft Azure, Python, SQL, and Databricks are essential for this position, and familiarity with both generative and agentic AI is highly valuable as we continue to advance our analytics capabilities.

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:

• Experience in Financial Crimes / Compliance Risk Analytics field
• Experience in generating data and analytics insights and assisting financial institutions with addressing the efficiency and effectiveness of transaction monitoring systems
• Experience with data transformation, ETL, and combining data from multiple sources to create analytics reports
• Hands-on experience developing, validating, and deploying machine learning models
• Experience with Microsoft Azure, Python, SQL, and Databricks
• Experience with both generative and agentic AI

Customer Accountabilities:

• Understands business context and data infrastructure and translates business problems to viable data science solutions
• Uses a wide range of programing languages (e.g. Python) and techniques for extracting and preparing data, applying statistics and various advanced analytics, along with business acumen to extract insights from the big data
• Visualizes insights fr