To build advanced fraud prediction models to prevent fraud in various aspects of PayPal's business, including identity, onboarding, authentication, abuse, scam and product-specific models.
Requirements
- Familiarity with ML frameworks like TensorFlow or scikit-learn
- Strong analytical and problem-solving skills
- Minimum of 2 years of relevant work experience and a Bachelor's degree or equivalent experience
Responsibilities
- Design and implement core decision models for identity, onboarding, authentication, abuse, scam, product-specific models
- Develop and refine algorithms for detecting anomalies and identifying potential fraud patterns
- Apply supervised learning techniques to build predictive models that accurately identify fraudulent activities
- Utilize continual learning methods to continuously improve model performance and adapt to new fraud tactics
- Collaborate with cross-functional teams to integrate fraud prediction models into various systems and processes
- Conduct experiments, analyze results, and interpret findings to drive innovation and enhance decision-making processes
Other
- Minimum of 2 years of relevant work experience and a Bachelor's degree or equivalent experience
- Ability to work in a hybrid work model with 3 days in the office and 2 days at home
- Commitment to diversity and inclusion
- Ability to thrive in a fast-paced environment
