Software Engineering Manager

DataVisor is a next-generation security company that utilizes industry-leading unsupervised machine learning to detect fraudulent activity for financial transactions, mobile user acquisition, social networks, commerce, and money laundering. Our solution is used by many industry-leading companies, to protect them from the ever-increasing risk of fraud.

DataVisor holds a rich product portfolio of AI as a platform and other SaaS products, such as feature platform, dEdge, rule engine, case management, and dCube. Our award-winning software is powered by a team of world-class experts in big data, 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, Flink, Cassandra) to build large scale machine learning pipelines

• Develop new systems on top of real-time streaming technologies (e.g. Kafka, Flink)

• Manage and coach team members