Come work at a place where innovation and teamwork come together to support the most exciting missions in the world!
Today, as part of F5, Shape Security products identify and stop automated attacks and fraud against web and mobile applications of the Fortune Global 2000.
Tomorrow, we’ll need to fight even more sophisticated adversaries, while helping human users to have ever more frictionless experience.
Come be a part of our unparalleled team that is responsible for making the Internet a safer place for everyone.
WE are seeking a driven, analytical and highly professional data scientist to help deliver our anti-fraud solution to customers, and to shape the future of what we build.
You will enjoy working with one of the richest data sets in the world, cutting-edge technology, and the opportunity to train and deploy machine learning models to fight fraud, in real-time and on highly visible web and mobile applications.
This Data Scientist will be part of a team that is responsible for delivering Shape’s anti-fraud solution to customers. You’ll have an opportunity to engage closely with customers, and become an expert in fraud across multiple use cases and industry verticals.
We are looking for a data scientist who has a nose for identifying fraud and anomalous patterns in huge and often messy datasets, and who is adept at using these insights to build high-performance machine learning models.
The ideal candidate will also have a penchant for working closely with enterprise customers, and a desire to deliver outstanding value to them.
The ideal candidate would have :
g. commonly found in fraud use cases) a strong plus.
Prior experience working with customers on complex fraud or security issues would be a strong plus.
Shape and F5 Networks are equal opportunity employers and we embrace diversity.
The Job Description is intended to be a general representation of the responsibilities and requirements of the job. However, the description may not be all-inclusive, and responsibilities and requirements are subject to change.