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DataVisor is the world’s leading AI-powered Fraud and Risk Platform that delivers the best overall detection coverage in the industry. With an open SaaS platform that supports easy consolidation and enrichment of any data, DataVisor's solution scales infinitely and enables organizations to act on fast-evolving fraud and money laundering activities in real time. Its patented unsupervised machine learning technology, advanced device intelligence, powerful decision engine and investigation tools work together to provide guaranteed performance lift from day one. DataVisor's platform is architected to support multiple use cases across different business units flexibly, dramatically lowering the total cost of ownership, compared to legacy point solutions. DataVisor is recognized as an industry leader and has been adopted by many Fortune 500 companies across the globe.

Our award-winning software platform is powered by a team of world-class experts in big data, machine learning, security, and scalable infrastructure. Our culture is open, positive, collaborative, and results driven. Come join us!

Summary

As platform engineers, we are building a next-generation machine learning platform, which incorporates our secret sauce, UML (unsupervised machine learning) with other SML (supervised machine learning) algorithms. Our team works to improve our core detection algorithms and automate the full training process.

As complex fraud attacks become more prevalent, it is more important than ever to detect fraudsters in real-time. The platform team is responsible for developing the architecture that makes real-time UML possible. We are looking for creative and eager engineers to help us expand our novel streaming and database systems, which enable our detection capabilities.

We continue to push the boundary of what's possible in fraud detection and data processing at scale. Join us to help usher in more innovative solutions to the fraud detection space.

What you'll do

• Design and build machine learning systems that process data sets from the world’s largest consumer services
• Use unsupervised machine learning, supervised machine learning, and deep learning to detect fraudulent behavior and catch fraudsters
• Build and optimize systems, tools, and validation strategies to support new features
• Help design/build distributed real-time systems and features
• Use big data technologies (e.g. Spark, Hadoop, HBase, Cassandra) to build large scale machine learning pipelines
• Develop new systems on top of real-time streaming technologies (e.g. Kafka, Flink)

Requirements