ID.me is looking to hire a Data Scientist to help protect millions of people from online fraud by uncovering fraud patterns, designing machine learning solutions, and developing strategies that safeguard digital identities across their network.
Requirements
- Strong technical skills in Python, SQL, and data analysis libraries (pandas, numpy, scikit-learn).
- Familiarity with machine learning methods such as decision trees, ensemble models, and deep learning frameworks (e.g., TensorFlow, PyTorch, Keras).
- Experience working with large datasets through coursework, research projects, or internships.
Responsibilities
- Design experiments, test hypotheses, and analyze fraud patterns to improve detection accuracy.
- Build models, rules, and dashboards to detect fraud with minimal false positives.
- Respond to fraud attacks by rapidly developing monitoring tools and scalable solutions.
- Leverage member interaction data to identify signs of stolen PII, account takeover, and social engineering attempts.
- Establish monitoring frameworks to track and improve fraud detection processes.
- Partner across teams to ensure insights drive both immediate fraud response and long-term prevention.
Other
- Master’s degree in Data Science, Applied Mathematics, Statistics, Computer Science, or related quantitative field.
- Data-driven thinker with excellent problem-solving skills and the ability to communicate insights clearly.
- Excited to apply advanced analytics to real-world challenges in fraud prevention and digital security.
- This role is designed for graduate students who want to apply their data science and quantitative skills to one of the most pressing challenges in tech: preventing fraud and protecting digital identities.
- In-Office Growth: While flexibility exists, this role is primarily in-office (McLean, VA or Mountain View, CA), providing opportunities for mentorship, collaboration, and long-term career development.
