Fraud Modelling/Forecasting Specialist (with Data Graph Expertise)

Work Location:

Mount Laurel, New Jersey, United States of America

Hours:

40

Pay Details:

$95,264 - $155,376 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 Fraud Modeling/Forecasting Specialist (with Graph Data Expertise) leverages a solid understanding of graph theory and advanced analytics to develop innovative fraud detection models and strategies across multiple products. Responsibilities include close collaboration with cross-functional teams to design, implement, and monitor fraud mitigation solutions, using cutting-edge graph technologies such as Databricks and TigerGraph. The position entails analyzing complex data structures, identifying trends, and continuously looking for new opportunities and data sources to enhance fraud detection capabilities. The ideal candidate will have strong experience in fraud detection, a deep understanding of graph databases, and a passion for solving complex problems in a rapidly changing environment.

This is a Remote career opportunity with less than 10% travel requirements.

Monday-Friday 8:00am to 5:00pm Eastern Standard Time

Depth & Scope:

• Acts as a subject matter expert integrating cross function understanding within their own field of specialty

• Works autonomously and accountable for acting as a lead within a specialized analytics function and may provide work direction to others

• Provides seasoned specialized knowledge, advice and/or guidance to various stakeholders and team members

• Expert at utilizing data sources across the organization with ability to integrate data across multiple platforms

• Works effectively across multiple business units with numerous stakeholders to deliver advanced analytics solutions

• Scope of role may have business segment and/or enterprise impact

• General focus on broad range of complex issues that may span from medium – long term issues (e.g. 6-12 months)

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;

• 5+ year of relevant experience; higher degree education and research tenure can be counted.

Preferred Experience:

• Graph Theory Expertise: Strong knowledge of graph algorithms and their practical applications in fraud detection

• Graph Databases: Experience with graph databases such as TigerGraph, Neo4j, as well as cloud platforms like Databricks for graph analytics.

• Fraud Detection: In-depth understanding of different types of fraud (e.g., account takeover, money laundering, transaction fraud) and the ability to apply graph-based models for identifying suspicious patterns.

• Data Analysis and Visualization: Ability to perform data wrangling, data exploration, and visualization of complex graph structures to highlight fraud risks and trends. Tools such as Python, R, SQL, Tableau, or Power BI may be used for these tasks.

• Machine Learning & Statistical Analysis: Expertise in building machine learning models for fraud detection, including supervised/unsupervised models, and familiarity with anomaly detection techniques specific to graph data.

• Big Data Processing: Experience with distributed computing platforms like Spark (via Databricks) for processing large datasets in a graph context.

Customer Accountabilities:

• Works closely with business management on data modelling requests/activities to ensure alignment with overall strategies

• Leads the activities related to d