This role pays more than
99%
of similar roles. Most pay
$212,500–$285,086
— the shaded band above.
At the midpoint, this role pays about
$404k
versus about
$249k
for comparable roles.
Based on 240 similar postings.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
1150 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 912 roles with salary data.
As the Director, FSI Predictive Technology, you will lead the technical strategy, architecture, and roadmap for scalable fraud technology and platforms. Working at the intersection of machine learning, data engineering, security, risk, and distributed systems, you will build reusable detection infrastructure, APIs, and frameworks to help customers identify emerging threats and reduce false positives. You will design detection approaches incorporating rules, machine learning, anomaly detection, behavioral analytics, graph analytics, entity resolution, and risk scoring. Your daily work involves integrating fraud signals across transactional, identity, device, and network data while translating complex patterns into production-grade systems. You will collaborate with cross-functional teams to develop tools for real-time decisioning and automated feedback loops. The role focuses on solving the technical challenge of detecting adaptive fraud behavior through advanced analytics and high-volume, event-driven data processing systems.
Integrate diverse fraud signals from transactional, identity, device, and behavioral data into production systems.
Establish frameworks to rapidly translate new fraud patterns into automated rules and detection workflows.
Monitor and improve detection effectiveness using metrics like precision, recall, and false-positive rates.
Lead technical root-cause analysis to improve system resilience when fraudulent activity bypasses existing controls.
Build reusable infrastructure, APIs, and reference architectures to support multiple products and customer environments.
Evaluate emerging technologies like AI and graph analytics to enhance fraud detection and analyst tools.
What we're looking for
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 products.
Deep expertise in 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 for internal or external customer use.
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.
Python
R
SQL
Machine Learning
Deep Learning
Natural Language Processing
Generative AI
Large Language Models
MLOps
CI/CD
Data Engineering
Data Visualization
Reinforcement Learning
Large-scale Model Training
GPU Programming
PPA Optimization
AI Systems
Confidential Compute
Compiler
Machine Learning
Training Pipelines
Microsoft Power BI
Microsoft Fabric
SQL
Artificial Intelligence
Data Architecture
Scenario Modeling
Advanced Analytics
Business Intelligence
Self-Service Analytics
Data Strategy