Teams at DataVisor

Develop and productionize fraud detection machine learning models using large-scale behavioral, device, network, and transaction data. Lead investigations into complex fraud attacks, reconstruct attacker behavior, identify trends, and produce technical reports and customer-facing insights. Collaborate with product, engineering, operations, legal, executives, and customers to improve detection systems and automate analytic workflows.

Lead the technical direction and hands-on development of a real-time fraud detection platform, including streaming, storage, distributed systems, machine learning training pipelines, and AI-assisted engineering tools. The role drives architecture, technical planning, operational health, incident prevention, production support, and collaboration with Product and customer-facing teams. It also mentors engineers, establishes AI-assisted development standards, and evaluates LLM- and agent-based tools for engineering operations.

Design and build large-scale machine learning and real-time streaming systems to detect fraud using unsupervised, supervised, and deep learning; build pipelines using big data technologies and optimize distributed systems for real-time detection.