AML Quantitative Analyst, Data Scientist II

Work Location:

Mount Laurel, New Jersey, United States of America

Hours:

40

Pay Details:

$76,128 - $124,800 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:

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 and Role 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:
• Exceptional candidate with master's degree in data science or statistics
• Proficiency with Python and SQL coding is a must
• Hands-on experience in advanced quantitative analyses and modeling

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 from the data to tell and illustrate stories that clearly convey the meaning of results to decision-makers and stakeholders at every level of technical understanding
• Collaborates with other partners, such as data and business analysts, software engineers, data engineers, and application developers to develop scalable and sustainable data science solutions that retains long term benefit to the business

Shareholder Accountabilities:

• Solicits and offers ideas for improving business processes through insights with the objective of improving effectiveness and efficiency
• Educates the organization on approaches, such as testing hypotheses and statistical validation of result
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