Data Scientist - Fraud Detection

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 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.

The Data Science/Modeling 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 big data software engineers and inquisitive data scientists. We love finding beautiful patterns in data to catch and prevent malicious attacks against normal 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 mining fraud patterns while wrangling big data, are excited by building world class anti-fraud models, and want to work on a team that impacts the company’s bottom line, we’d love to talk to you.

• This is an entry-level position ideal for recent graduates. We welcome candidates with an advanced degree (Master’s or Ph.D.) who are eager to apply their knowledge and skills in a professional setting. No prior full-time experience is required, though relevant internships or research experience are a plus.
• Hands-on project or research experience in applying machine learning techniques to solve real-world problems. Solid understanding of machine learning algorithms, statistical methods, and data structures.
• Strong skills in large-scale data manipulation, data mining, and data processing.
• Proficiency in Python and/or Java, SQL, and commonly used data science tools (such as NumPy, Pandas, Scikit-learn, etc.).
• Strong analytical and problem-solving skills, with attention to detail.
• Excellent communication skills and ability to collaborate in a team-oriented environment.
• Plus/Preferred Qualifications:
• Ph.D. in Computer Science, Data Science, or a related engineering field.
• Experience working with big data technologies such as Hadoop, MapReduce, or Spark.
• Background in financial crime detection and fraud analysis within a financial institution or payment solutions company.