Lead Data Analyst - Risk Management Specialist
This role involves leading new projects to build and improve risk strategies that prevent fraud, leveraging both proprietary tools and custom AI/ML models. As a key member of the Fraud Risk team, you will partner with business units to align with strategic priorities, educate partners about risk-management principles, and collaboratively optimize risk treatments and customer experiences for targeted products and partnerships.
The ideal candidate has 2-4 years of experience working with large-scale datasets, preferably in a regulated environment such as financial services or fraud-risk. Strong analytical skills, excellent communication abilities, and a passion for building innovative solutions are essential.
Key Responsibilities:
* Taking full responsibility for a merchant portfolio's fraud and loss metrics, ensuring end-to-end management of decline and loss rates.
* Developing fraud-prevention strategies, identifying loss-savings opportunities, and optimizing transaction declines without increasing customer friction.
* Delivering risk analytics on frustration trends and key performance indicators, setting up alerts for fraud events, and monitoring real-time dashboards to detect anomalies.
* Adapting PayPal's advanced proprietary fraud-prevention tools, combining them with custom data and AI/ML techniques, to enable continued business growth.
* Providing clear, data-driven requirements to Data Science and Technology teams for attribute engineering, model specifications, and platform improvements, while keeping global stakeholders informed of performance and next steps.
Requirements:
* Strong ability to decompose business requirements into a structured analytic plan, execute that plan end-to-end, and derive actionable insights.
* Excellent verbal and written skills, equally comfortable discussing technical details with engineers and high-level risk strategies with business leaders.
* A desire to build new solutions, invent novel approaches to big, ambiguous challenges, and iterate quickly as new fraud trends emerge.
* Solid working knowledge of Excel, SQL, and Python or R for data wrangling, exploration, and analysis.
* Familiarity with exploratory data analysis and preparing clean, structured datasets for model development.
* Hands-on experience applying AI/ML techniques—both supervised (regression, classification, decision trees) and unsupervised (clustering, anomaly detection)—for business decisioning.
* Familiarity with model evaluation metrics (Precision, Recall, ROC-AUC) and basic statistical concepts.
Benefits:
We offer flexible work arrangements, employee share options, health and life insurance, and more. Our employees thrive in an inclusive environment where they can do their best work with a sense of purpose and belonging. Join our community today!
],