Fraud Data Scientist, AIML Modeling & GenAI

Job title: Fraud Data Scientist, AIML Modeling & GenAI in USA at HealthEquity

Company: HealthEquity

Job description: OverviewHow you can make a differenceYou will drive HealthEquity's predictive modeling for card fraud, money-movement scams, AML alerts, and account-takeover attacks. You will also help drive our GenAI portfolio for fraud and security, you’ll build models that predict fraud in real time, craft AI assistants that automate alert triage and policy drafting, and deliver explainable insights that drive both immediate containment and long-term strategic controls. Your innovations will be mission-critical in reducing losses, scaling defenses, and elevating HealthEquity’s security and fraud posture.What you’ll be doing

• Predictive Modeling & Feature Engineering: Build supervised (XGBoost, neural nets) and unsupervised (autoencoders, isolation forests) models for CNP, AML, and Account Takeover/identity fraud. Engineer sophisticated features—device fingerprints, transaction sequences, behavior embeddings, geospatial velocity —optimized for real-time scoring.


• GenAI Solutions: Design and fine-tune LLM-based assistants for alert summarization, adaptive rule suggestions, and dynamic MFA policy drafting. Manage vector stores and retrieval pipelines to ensure rapid, accurate GenAI responses under load.


• MLOps & Production Integration: Define end-to-end model lifecycle: data ingestion, versioned training, CI/CD deployment, monitoring for drift, and automated retraining triggers. Collaborate with Product Technology & Infrastructure team to integrate inference endpoints into transaction gateways and real-time streaming platforms.


• Explainability & Compliance: Implement explainable-AI frameworks so that every alert can be traced, justified, and audited for regulatory reviews. Partner with Compliance to document model risk assessments and satisfy any aligned internal or external regulations.


• Performance Metrics & Continuous Improvement: Establish dashboards tracking capture rates, false-positive lift, investigation velocity, and GenAI assistant adoption. Iterate on models and GenAI prompts based on feedback loops from confirmed fraud cases and investigations.

What you will need to be successful

• Master’s or Ph.D. in Data Science, Machine Learning, Statistics, or related field.


• 3+ years applying AI/ML to fraud prevention, AML, or risk-analytics contexts.


• Proficient in Python, SQL, and ML frameworks (scikit-learn, XGBoost, TensorFlow/PyTorch), and large-language-model fine-tuning.


• Demonstrated ability to build production-ready MLOps pipelines, from data ingestion to real-time inference.


• Strong collaboration and storytelling skills— able to articulate model logic, performance, and business impact to technical and non-technical stakeholders.


• Deep knowledge of payment card networks, EFT systems, and money movement


• Experience with streaming data architectures (Kafka, Spark structured streaming) and real-time feature stores


• Prior work integrating GenAI assistants into analyst workflows or customer-facing applications


• Familiarity with software engineering best practices: code reviews, unit testing, and CI/CD


• Open-source contributions or publications in fraud detection, AML, or generative AI


• Background in fast-faced fintech or payments environments, and a passion for staying ahead of adversaries

#LI-RemoteThis is a remote position.Salary Range$115000.00 To $18000.00 / yearBenefits & PerksThe actual compensation offer is determined based on job-related knowledge, education, skills, experience, and work location. This position will be eligible for performance-based incentives as part of the total compensation package, in addition to a full range of benefits including:

• 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

Why work with HealthEquityWhy work with HealthEquityHealthEquity has a vision that by 2030 we will make HSAs as wide-spread and popular as retirement accounts. We are passionate about providing a solution that allows American families to connect health and wealth. Join us and discover a work experience where the person is valued more than the position. Click to learn more.You belong at HealthEquity!HealthEquity, Inc. is an equal opportunity employer, and we are committed to being an employer where no matter your background or identity – you feel welcome and included. We ensure equal opportunity for all applicants and employees without regard to race, age, color, religion, sex, sexual orientation, gender identity, national origin, status as a qualified individual with a disability, veteran status, or other legally protected characteristics. HealthEquity is a drug-free workplace. For more information about our EEO policy, or about HealthEquity’s applicant disability accommodation, drug-free-workplace, background check, and E-Verify policies, please visit our .HealthEquity uses Microsoft Copilot to transcribe screening interviews between candidates and their direct Talent Partner for note taking and interview summaries. By scheduling a screening interview with us, you consent to Microsoft Copilot’s AI technology recording and transcribing your interview with your Talent Partner. This information will be reviewed for accuracy and then used by HealthEquity to summarize the interview, ensure accuracy, and facilitate our hiring process. We take privacy seriously. You have the option to opt out. If you wish to opt out of this Microsoft Copilot transcription, please notify your Talent Partner in advance of the interview. If we do not receive an opt-out request from you, we will assume that you consent to the use of Microsoft Copilot.HealthEquity is committed to your privacy as an applicant for employment. For information on our privacy policies and practices, please visit .

Expected salary: $18000 per year

Location: USA

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