Fraud Data Scientist
| Company | ID.me |
|---|---|
| Location | McLean, VA, USA, Mountain View, CA, USA |
| Salary | $136301 – $156000 |
| Type | Full-Time |
| Degrees | Master’s |
| Experience Level | Mid Level, Senior |
Requirements
- 2+ years of hands-on experience in fraud analytics or a high-tech mature startup
- Experience with deep learning frameworks such as Tensorflow, Tensorflow Recommenders, Pytorch, MXNet, Keras
- MS in a highly quantitative field (Computer Science, Engineering, Math, Operations Research, Physics, Statistics, or related)
- 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.
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
Preferred Qualifications
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No preferred qualifications provided.