PayPal is looking to tackle high-impact challenges that shape how millions of people move and manage money with friends, family, and businesses on Venmo. This involves improving personalization, retention, trust, discovery, and growth by leveraging ML solutions to drive engagement, safety, and meaningful product experiences, with a growing focus on agentic AI.
Requirements
- Strong proficiency in Python and experience with modern ML libraries (e.g., scikit-learn, TensorFlow, PyTorch, XGBoost).
- Expertise in supervised and unsupervised learning; experience with NLP, ranking systems, or user behavior modeling is a plus.
- Proficiency in SQL and experience working with large-scale data systems in cloud-based environments (e.g., BigQuery, Spark, Airflow).
- Demonstrated success deploying ML models in production, including monitoring, retraining, and debugging.
- Familiarity with real-time ML model serving and streaming infrastructure.
- Contributions to research publications or open-source ML projects.
Responsibilities
- Design, build, and deploy production-grade machine learning models that power personalization, search, ranking, discovery, and other core product experiences.
- Use machine learning to drive monetization and deliver user value—supporting initiatives such as pricing strategy, feature optimization, and behavioral targeting.
- Partner with product managers, engineers, and designers to identify high-impact opportunities, frame problems as ML tasks, and define measurable success.
- Lead the full lifecycle of model development—from data exploration and feature engineering to validation, deployment, and monitoring in production.
- Conduct rigorous offline evaluations and design online experiments (e.g., A/B tests) to assess model impact and guide iteration.
- Collaborate with data and platform engineering teams to ensure scalable infrastructure, low-latency serving, and long-term system maintainability.
- Communicate technical insights and data-driven recommendations clearly to both technical and non-technical stakeholders.
Other
- Advanced degree (M.S. or Ph.D.) in a quantitative field such as Computer Science, Machine Learning, Statistics, or Applied Mathematics.
- 8+ years of industry experience applying machine learning to real-world problems at scale, preferably in product or platform settings.
- Excellent problem-solving and communication skills, with the ability to translate complex technical ideas into actionable product insights.
- A collaborative mindset with a passion for mentoring, experimentation, and continuous improvement.
- Experience in social platforms, fintech, consumer apps, or payments ecosystems.
