Job Summary
Decision scientists in the Authentication Risk team are problem solvers suited to approach varied challenges in complex big data environments. Our core goals are to enable seamless and delightful authentication experiences to our customers, while preventing threat actors from accessing customers financial instruments and personal information, and adhering with regional authentication regulations.
Job Description
Essential Responsibilities
- Lead the development and implementation of advanced data science models.
- Collaborate with stakeholders to understand requirements.
- Drive best practices in data science.
- Ensure data quality and integrity in all processes.
- Mentor and guide junior data scientists.
- Stay updated with the latest trends in data science.
Minimum Qualifications
- Minimum of 5 years of relevant work experience and a Bachelors degree or equivalent experience.
Preferred Qualification
- Strong working knowledge of Excel, SQL and Python/R
- Partner with Product Managers and Engineers to define test hypotheses, success metrics, and experimentation frameworks
- Proactively identify opportunities for experimentation to optimize conversion rates, revenue, and user enablement
- Maintain documentation of experiments and contribute to building a culture of data-informed decision making
- Exploratory Data Analysis and expertise in preparing a clean and structured data for model development
- Experience in applying AI/ML techniques for business decisioning including supervised and unsupervised learning (e.g., regression, classification, clustering, decision trees, anomaly detection, etc.)
- Knowledge of model evaluation techniques such as Precision, Recall, ROC-AUC Curve, etc. along with basic statistical concepts
- Payments fraud domain knowledge is highly preferred, with an understanding of transaction flows, payment systems, and common fraud tactics.
- Experience in conducting & analyzing A/B tests to evaluate the impact of fraud detection models and mitigation strategies.
- Familiarity with common fraud data sources such as transactions, user behavior logs, device metadata, and third-party data
Subsidiary
PayPal
Travel Percent
0
Our Benefits
Who We Are
Belonging at PayPal