Fraud Data Scientist

Role Overview

We are seeking a Fraud Data Scientist to gather critical insights and identify and analyze fraud patterns across the ID.me network. In this role, you will work closely with the Fraud Analytics Team, Fraud Investigations Team, Engineering, Product and Customer Success to execute on ID.me’s fraud strategy.  If you are data-driven, results-oriented, and eager to help solve data problems related to fraud, then this would be the perfect opportunity for you.

Responsibilities

• Partner with fraud leadership and fraud investigators to develop our fraud strategy
• Design experiments, test hypotheses, and analyze fraud patterns that are effective at detecting fraud with low false positive rates
• Leverage data analytics evaluate, recommend, and manage fraud strategies to prevent fraudulent activity on the ID.me network
• Respond quickly to fraud attacks by developing fraud monitoring frameworks, dashboards, and solutions in collaboration with cross-functional teams
• Recommend and build automated rules and models to support the detection and prevention of fraudulent activity
• Use signals and data collected by member interactions with the ID.me network to identify the use of stolen Personal Identifiable Information (PII), social engineering, and account takeover (ATO)
• Establish robust monitoring capabilities to ensure high performance of both automated and manual fraud detection processes
• Build strong relationships with key partners and the leadership

Qualifications

• 2+ years of hands-on experience in fraud strategy or a high-tech mature startup
• 2+ years of experience in fraud analytics or related 
• Experience with deep learning frameworks such as Tensorflow, Tensorflow Recommenders, Pytorch, MXNet, Keras
• MS (PHD Preferred) in a highly quantitative field (Computer Science, Engineering, Math, Operations Research, Physics, Statistics, or related)
• Experience building & deploying ML products on GCP and/or AWS
• Experience with SQL & the Python ML ecosystem - pandas, numpy, sklearn, etc. 
• Experience with Time Series Prediction models & one or more deep learning libraries
• Experience in developing, managing, and manipulating large, complex datasets
• Data-driven, detail-oriented individual with excellent storytelling and problem-solving abilities
• Ability to work independently and autonomously, as well as part of a team
• Superb time management, prioritization of tasks and ability to meet deadlines with little supervision

Note that candidates must be located in the continental U.S.