PayPal is looking to solve the complex and evolving landscape of local and global regulations by leveraging advanced machine learning, cutting-edge research, and data-driven insights to design innovative solutions that streamline compliance processes, strengthen risk mitigation, and empower PayPal to deliver a secure, trusted, and seamless financial experience worldwide.
Requirements
- Strong theoretical foundation in ML algorithms, optimization, and statistical learning theory.
- Demonstrated ability to implement and evaluate ML models using Python and libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch.
- Experience conducting independent research, with publications in relevant ML/AI conferences or journals (preferred).
- Currently pursuing a PhD in Computer Science, Machine Learning, Statistics, or a related field.
- Excellent communication and collaboration skills, with the ability to present research to both technical and non-technical audiences.
- Highly motivated, curious, and proactive in exploring new research directions.
- Authorized to work in the U.S. for the duration of the internship.
Responsibilities
- Conduct applied research in machine learning and AI, focusing on novel methods for fraud detection, AML/KYC, regulatory reporting, and risk modeling.
- Investigate and prototype cutting-edge algorithms (e.g., deep learning, graph neural networks, reinforcement learning, generative models) to solve high-impact financial security problems.
- Collaborate with engineers, data scientists, and domain experts to translate business and regulatory needs into scalable ML frameworks.
- Perform advanced data analysis and experimentation to evaluate robustness, fairness, and interpretability of models in sensitive financial contexts.
- Contribute to the design and documentation of research-driven ML pipelines that emphasize reproducibility, scalability, and rigor.
- Disseminate findings through technical reports, internal presentations, and stakeholder discussions, demonstrating both theoretical and practical value.
- Explore emerging areas such as NLP for regulatory text analysis, graph learning for transaction networks, and causal inference in compliance and risk systems.
Other
- Must be enrolled in a PhD program at an accredited university, returning to studies after the internship.
- Must reside in the U.S. during the program.
- Must be authorized to work in the U.S. for the duration of the internship.
- Currently pursuing a PhD in Computer Science, Machine Learning, Statistics, or a related field.
- Excellent communication and collaboration skills, with the ability to present research to both technical and non-technical audiences.
