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Fraud Data Scientist, Graph Analytics and Threat Intelligenc

HEALTHEQUITY, INC. • Draper, UT • 5h ago

Fraud Data Scientist, Graph Analytics and Threat Intelligence

Job Locations

US-Remote

Our Mission

Our mission is to SAVE AND IMPROVE LIVES BY EMPOWERING HEALTHCARE CONSUMERS.Come be part of remarkable.

Overview

How you can make a difference

You will be the architect of our next-generation threat-hunting and fraud-ring-detection capabilities-building graph-based analytics pipelines and Generative AI assistants that spot hidden networks of bad actors before losses occur. You will also help drive our Fraud and Security GenAI portfolio, deliver interactive chatbots, narrative generators, and automated playbooks that empower analysts to move at machine speed. This is a game-changing role: you'll fuse transactional, identity, and threat-intel data into actionable network insights, partner on coordinated takedowns with Investigations and law enforcement, and set the vision for how HealthEquity outsmarts organized fraud.

What you'll be doing

Graph and Network Modeling: Design and productionize end-to-end pipelines that ingest transactions, identity records, and external intelligence into a graph store. Implement community detection, centrality scoring, and path-analysis algorithms to surface rings of colluding accounts or synthetic identities.

GenAI Assistant Development: Lead Development of retrieval-augmented LLM tools to answer analyst queries ("Show me all accounts linked to this phone"), auto-summarize case dossiers, and recommend next investigative steps. Continuously refine prompts, vector stores, and retrieval strategies for high precision in a fraud @context.

Threat Hunting and Dashboards: Build interactive notebooks and visualizations for ad-hoc threat exploration-enabling analysts to pivot from summary graphs to transaction-level detail in seconds. Define and track KPIs: time-to-detect, ring-size reduction, and take-down success rates.

Cross-Functional Collaboration and Takedowns: Partner with Internal Investigations, FBI, and Secret Service on evidence gathering and synchronized fraud-ring takedown operations. Ingest post-takedown intel to retrain models and evolve detection patterns.

Innovation and Mentorship: Proto@type advanced techniques (graph neural networks, anomaly detection) to predict emerging attack tactics. Mentor junior data scientists on graph theory, MLOps best practices, and GenAI usage.

Sample Success Metrics Detection of top fraud rings pre-funding, reducing associated losses by target. Decrease in time investigators spend manually linking entities by target. Accuracy on synthetic-ID cluster classification by target.

What you will need to be successful

Master's or Ph.D. in Data Science, Computer Science, Applied Math, or related.

Expertise in fraud or security analytics focused on graph or network methods.

Expert in Python, SQL, and graph frameworks.

Strong data engineering skills building ETL pipelines, managing large-scale datasets, and ensuring data quality

Demonstrated ability to translate complex analytical results into clear, actionable insights

Experience with MLOPs frameworks for model versioning, monitoring, and automated retraining

Strong communicator with a track record of driving cross-team initiatives.

Experience with deepfake detection and adversarial-resilient modeling.

Hands-on experience designing and deploying retrieval-augmented GenAI (LLM integration, vector search) solutions in an enterprise setting.

Background and expertise in fintech, EFT, AML, payment-card fraud analytics.

Proven collaboration in high-urgency environments, able to support live investigations and coordinate r

apid takedowns.

Prior work (open source, or academic publications) around analytics or security AI portfolio and usecases

LI-Remote

This is a remote position.

Salary Range

$115000.00 To...For full info follow application link.

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