About the job
DataVisor stands at the forefront of fraud prevention with its cutting-edge AI-powered Fraud and Risk Management Platform. Our platform is designed to provide unparalleled detection capabilities, seamlessly integrating and enriching diverse data sources. This scalable solution empowers organizations to respond to rapidly evolving fraudulent and money laundering threats in real time. Leveraging patented unsupervised machine learning technology, advanced device analysis, and a robust decision engine, our platform guarantees performance enhancements from day one. DataVisor's architecture supports a variety of use cases across multiple business units, significantly reducing overall ownership costs compared to traditional point solutions. Recognized as an industry leader, DataVisor is trusted by numerous Fortune 500 companies worldwide.
Our award-winning software platform is backed by a talented team of experts in big data, machine learning, security, and scalable infrastructure. We pride ourselves on fostering an open, positive, and collaborative culture that drives results. Join us as we redefine the landscape of fraud detection!
Role Overview:
As a Senior Software Engineer on our platform team, you will play a key role in developing next-generation machine learning systems that blend our proprietary unsupervised machine learning (UML) with other supervised machine learning (SML) algorithms. Your contributions will be vital in enhancing our core detection algorithms and automating the training processes.
With the rise of sophisticated fraud attacks, real-time detection has become critical. You will be instrumental in shaping the architecture that enables real-time UML functionality. We seek innovative engineers who are eager to expand our advanced streaming and database systems, enhancing our detection capabilities.
Join us in pushing the boundaries of fraud detection and large-scale data processing solutions!
Key Responsibilities:
- Design and develop machine learning systems that analyze data from some of the largest consumer services globally.
- Utilize unsupervised machine learning, supervised machine learning, and deep learning techniques to identify fraudulent activities and apprehend fraudsters.
- Create and optimize systems, tools, and validation strategies to facilitate new feature implementation.
- Contribute to the design and development of distributed real-time systems and functionalities.
- Employ big data technologies (e.g., Spark, Hadoop, HBase, Cassandra) to construct large-scale machine learning pipelines.
- Develop new systems utilizing real-time streaming technologies (e.g., Kafka, Flink).

