About Goldman Sachs

Goldman Sachs is a leading global investment banking, securities, and investment management firm providing financial services to corporations, financial institutions, governments, and individuals.

Goldman Sachs currently has 187 open roles on FindRole.

Listed pay typically runs $130,000–$250,000 across 60 roles with salary data.

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At a glance

TL;DR · Fraud Detection & AI/ML Strategy Vice President

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The VP of Fraud Detection Engineering will lead the strategic development of advanced AI and Machine Learning models to protect the Consumer Deposit business from fraud. This role involves architecting real-time detection systems that leverage multi-dimensional signals for identifying and mitigating various fraud vectors throughout the customer lifecycle, including acquisition, onboarding, and ongoing account management. Key responsibilities include designing ML models using frameworks like Gradient Boosted Trees and Graph Neural Networks, developing low-latency inference pipelines, and ensuring robust security measures during money movement transactions. The candidate will also oversee scalable infrastructure for high-throughput data processing and lead a team of specialists in financial security. Required skills encompass deep expertise in machine learning, proficiency in Python and PySpark, and knowledge of banking protocols such as ACH and ISO 20022, alongside strong leadership and analytical capabilities to manage complex business impacts effectively.

Skills

Python PySpark AWS SageMaker GCP Vertex AI Gradient Boosted Trees Transformers Graph Neural Networks Kafka Apache Spark PostgreSQL Redis CI/CD Prometheus Grafana Docker Kubernetes Terraform GitLab GitHub

What you'll do

Lead design, training, and deployment of ML models for real-time fraud detection.
Develop frameworks to ingest and synthesize multiple fraud signals for real-time insights.
Implement robust models to identify synthetic identities during customer acquisition.
Define technical standards for monitoring ACH, wire transfers to detect unauthorized activity.
Oversee engineering of high-throughput data pipelines with sub-second latency capabilities.
Establish rigorous back-testing and monitoring frameworks to track model performance.

What we're looking for

8+ years of software engineering or data science experience, with at least 6 years in senior leadership within Fraud or Risk Tech.
Deep expertise in supervised and unsupervised machine learning for anomaly detection and classification in imbalanced datasets.
Proficiency in Python, PySpark, modern ML frameworks, and cloud-native AI services like AWS SageMaker and GCP Vertex AI.
Strong understanding of banking protocols (ACH, ISO 20022) and identity verification standards (KYC/AML).
Proven ability to reduce fraud loss rates while maintaining a seamless customer experience.
Experience in developing scalable infrastructure for high-throughput data pipelines with sub-second latency.
Master’s degree in Computer Science, Statistics, Mathematics, or related quantitative field.