Fraud Data Scientist, Graph Analytics & Threat Intelligence
Job title: Fraud Data Scientist, Graph Analytics & Threat Intelligence in USA at HealthEquity
Company: HealthEquity
Job description: OverviewHow you can make a differenceYou 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 & 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 & 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 & 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 & 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 & Mentorship: Prototype 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.
- 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 rapid takedowns.
- Prior work (open source, or academic publications) around analytics or security AI portfolio and usecases
- Medical, dental, and vision
- HSA contribution and match
- Dependent care FSA match
- Uncapped paid time off
- Adventure accounts
- Paid parental leave
- 401(k) match
- Personal and healthcare financial literacy programs
- Ongoing education & tuition assistance
- Gym and fitness reimbursement
- Wellness program incentives
Expected salary: $115000 - 180000 per year
Location: USA
Apply for the job now!
[ad_2]
Apply for this job