Machine Learning Engineer - Fraud Detection

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 some of the largest internet properties in the world, including Pinterest, Synchrony Financial, AirAsia and PingAn, to protect them from the ever-increasing risk of fraud. 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!

The modeling / data science team holds the secret sauce of DataVisor. We run our advanced core unsupervised analytics engine and machine learning models on hundreds of billions of events from hundreds of millions of users. We are a mix of machine learning engineers and inquisitive data scientists. We love finding beautiful patterns in big data to catch and prevent malicious attacks against good users. We're also not afraid to get our hands dirty; we get deep satisfaction coming up with and implementing new ideas for improvements to our detection engine.
If you have a knack for wrangling big data, are excited by the idea of being part of new technological frontiers, and want to work on a team that impacts the company's bottom line, we'd love to talk to you.

• 2+ years experience with Java/C++
• Familiar with common data structures and algorithms. Be able to write efficient and optimized computer programs
• 1+ years experience with web backend or Hadoop/MapReduce/Spark
• 1+ years experience with machine learning/data science preferred
• Anti-fraud modeling in financial industry a plus
• Bachelor degree required, Master/PhD degree preferred

We offer a flexible schedule with competitive pay, equity participation and health benefits, along with catered lunch, company off-sites, and game nights, as well as the opportunity to work with a world class team.