WHY SOCURE?

At Socure, we’re on a mission—to verify 100% of good identities in real time and eliminate identity fraud from the internet.
Using predictive analytics and advanced machine learning trained on billions of signals to power RiskOS, Socure has created the most accurate identity verification and fraud prevention platform in the world. Trusted by thousands of leading organizations—from top banks and fintechs to government agencies—we solve real, high-impact problems at scale. Come join us!

ABOUT THE ROLE

Socure is the leading provider of digital identity verification and fraud prevention solutions, leveraging AI and machine learning to power the most accurate identity trust decisions. Our mission is to eliminate identity fraud and ensure online trust across industries.
We are seeking a Staff Data Scientist to join our Digital Intelligence team. In this role, you will drive the development of machine learning features and models that leverage device, network, and behavioral data to power fraud prevention and identity verification. You’ll work with rich, high-volume data from browser, mobile, and API traffic to surface meaningful insights and scalable risk signals. This is a great opportunity to own impactful projects, collaborate cross-functionally, and deepen your expertise in applied ML for device and behavioral intelligence.

WHAT YOU’LL DO

• Design and deploy advanced machine learning systems for device identification, anomaly detection, and fraud prevention—balancing precision, recall, and real-world adversarial dynamics.
• Lead the development of scalable data pipelines and production ML workflows using structured and unstructured telemetry (e.g., browser, mobile, session data).
• Investigate high-complexity signals (e.g., emulator use, spoofing, low-entropy fingerprints), applying advanced statistical methods and domain knowledge to detect fraud and abuse.
• Translate ambiguous business problems into modeling approaches, using a combination of supervised, unsupervised, and heuristic techniques.
• Partner with engineering, product, and risk teams to influence data architecture, signal collection, and strategic planning.
• Drive experimental design, A/B testing frameworks, and robust validation techniques to ensure model generalizability and long-term trust.
• Contribute to company-wide standards for ML explainability, risk evaluation, and feature logging.
• Document methodologies and communicate results effectively through dashboards, presentations, and reports for both technical and executive audiences.
• Mentor junior data scientists and lead cross-functional working groups.