Director, FSI Predictive Technology

Details

We are looking for a Technical Fraud Director to define and guide the technical direction for scalable fraud technology and platforms. The role includes developing reusable technical capabilities that customers use to build, customize, and operate fraud detection, prevention, investigation, and decisioning systems. It sits at the intersection of fraud prevention, machine learning, data engineering, security, risk, and distributed systems.

The role collaborates with Engineering, Data Analysis, Protection, Risk Management, Product Development, Client Engineering, and Operations teams to translate evolving fraud patterns into production-grade fraud technology for customer use. The successful candidate will help shape the technical foundation customers use to develop fraud systems that identify emerging threats at scale, improve detection quality, reduce false positives, and respond faster to adaptive fraud behavior.

Responsibilities

• Lead the technical strategy, architecture, and roadmap for fraud technology that supports customers in building, customizing, and operating fraud detection, prevention, investigation, and decisioning systems.
• Design reusable detection approaches combining rules, machine learning, anomaly detection, behavioral analytics, graph analytics, entity resolution, and risk scoring.
• Partner with Data Science, ML Engineering, Product, and Customer Engineering teams to develop, evaluate, deploy, and continuously improve fraud detection capabilities.
• Identify, prioritize, and integrate fraud signals across transactional, identity, account, device, network, application, behavioral, and operational data.
• Establish frameworks for translating newly discovered fraud patterns into production rules, signals, models, and detection workflows.
• Define and monitor detection effectiveness using precision, recall, false-positive rates, detection coverage, alert quality, latency, and business-impact metrics.
• Lead technical root-cause analysis when fraudulent activity bypasses existing controls and drive improvements to detection logic, data coverage, and system resilience.
• Build reusable detection infrastructure, services, APIs, reference architectures, and frameworks supporting multiple products, fraud types, customer environments, and business use cases.
• Evaluate emerging technologies and determine where AI, machine learning, graph analytics, automation, and analyst-assist tooling can improve fraud detection and response.
• Lead technical builds and architecture reviews, driving alignment across Development, Data Science, Security, Risk, Product, Customer Engineering, and Service Delivery teams.

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related technical field, or equivalent experience.
• 15+ years of progressive experience in software engineering or a related technical discipline.
• 6+ years of experience leading and managing complex, cross-functional engineering organizations and delivering high-impact technical products or platforms.
• Deep expertise in one or more of software engineering, fraud technology, risk systems, security engineering, machine learning, data science, or data engineering.
• Significant experience designing, building, or operating large-scale fraud, abuse, risk, security, detection, or machine learning systems.
• Experience developing platforms, products, APIs, services, or technical frameworks adopted by internal or external customers to build production systems.
• Strong understanding of rules-based, statistical, behavioral, anomaly-based, graph-based, and machine-learning detection methods.
• Experience designing real-time, high-volume, distributed, or event-driven data-processing systems.
• Experience with data pipelines, feature engineering, model inference, production ML systems, or decisioning platforms.
• Ability to identify meaningful signals within large and complex datasets and translate them into actionable detection capabilities.
• Experience defining metrics and using data to evaluate and improve detection-system effectiveness.
• Strong systems-thinking skills and the ability to turn ambiguous fraud, abuse, customer, or threat patterns into clear technical requirements and scalable solutions.
• Demonstrated experience leading complex technical initiatives across engineering, data, risk, product, and customer-facing teams.

Preferred Qualifications

• Experience with machine-learning-based fraud detection, anomaly detection, behavioral modeling, entity risk scoring, or adaptive risk systems.
• Experience with graph analytics, graph machine learning, entity resolution, link analysis, or identifying coordinated activity across complex networks of entities.
• Experience developing systems that detect adaptive or adversarial behavior.
• Experience applying generative AI or LLMs to fraud detection, investigations, threat analysis, case summarization, or analyst workflows.
• Experience designing fraud technology platforms supporting multiple products, organizations, geographies, customer environments, or fraud use cases.
• Experience designing low-latency inference, streaming, event-processing, or real-time decisioning systems.
• Experience developing automated feedback loops using confirmed fraud, investigation outcomes, customer disputes, chargebacks, or analyst decisions to improve models and detection logic.

Company and Benefits

NVIDIA develops technologies in artificial intelligence, high-performance computing, and visualization. The role offers a competitive base salary, equity, and benefits. NVIDIA is an equal opportunity employer and uses AI tools in its recruiting processes.

Applications will be accepted at least until September 26, 2026. This posting is for an existing vacancy.