Director, FSI Predictive Technology

NVIDIA•Us, CA

About The Position

We are looking for a Technical Fraud Director to define and guide the technical direction for scalable fraud technology and platforms. This role includes developing reusable technical capabilities that customers use to build, customize, and operate fraud detection, prevention, investigation, and decisioning systems. This role sits at the intersection of fraud prevention, machine learning, data engineering, security, risk, and distributed systems. You will collaborate 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. You 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. If you feel you are an engaged technical expert able to navigate architecture, data, modeling concepts, system building, customer needs, and multi-functional coordination, please apply today!

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, including 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 the following areas is required: 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 that are adopted by internal or external customers to build production systems.
Strong understanding of detection methodologies, including rules-based, statistical, behavioral, anomaly-based, graph-based, and machine-learning approaches.
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.
Demonstrated 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 multiple engineering, data, risk, product, and customer-facing teams.

Nice To Haves

Deep 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 and developing systems that detect adaptive or adversarial behavior where attack patterns change in response to existing controls.
Experience applying generative AI or LLMs to fraud detection, investigations, threat analysis, case summarization, or analyst workflows and designing fraud technology platforms that support multiple products, organizations, geographies, customer environments, or fraud use cases rather than individual point solutions.
Experience designing low-latency inference, streaming, event-processing, or real-time decisioning systems.
Experience developing automated feedback loops that use confirmed fraud, investigation outcomes, customer disputes, chargebacks, or analyst decisions to improve models and detection logic.

Responsibilities

Lead the technical strategy, architecture, and roadmap for fraud technology built to scale and support customers in building, customizing, and operating fraud detection, prevention, investigation, and decisioning systems.
Design reusable detection approaches that combine 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 for customer use cases.
Identify, prioritize, and integrate fraud signals across transactional, identity, account, device, network, application, behavioral, and operational data.
Establish frameworks for rapidly translating newly discovered fraud patterns into production rules, signals, models, and detection workflows that can be adopted across customer environments.
Define and monitor detection effectiveness using metrics such as precision, recall, false-positive rates, detection coverage, alert quality, latency, and business impact.
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 that support 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 build and architecture reviews, driving alignment across Development, Data Science, Security, Risk, Product, Customer Engineering, and Service Delivery collaborators.