PayPal Data Scientist - Dublin 

Ireland, Dublin 

636305696

What You Need to Know About the Role
Each Data Scientist on this team has full ownership of a portfolio of merchants and is responsible for end-to-end management of loss and decline rates. Day-to-day duties include data analysis, monitoring and forecasting, creating and implementing risk rules and strategies, providing requirements to data scientists and technology teams on attribute, model, and platform needs, and communicating with global stakeholders to ensure the best possible customer experience while meeting loss-rate targets.

Day-to-Day Responsibilities:

In your day to day, you will:

• Take full responsibility for a merchant portfolio’s fraud and loss metrics, ensuring end-to-end management of decline and loss rates.

• Work closely with cross-functional teams to develop fraud-prevention strategies, identify loss-savings opportunities, and optimize transaction declines without increasing customer friction.

• Deliver risk analytics on frustration trends and key performance indicators, set up alerts for fraud events, and monitor real-time dashboards to detect anomalies.

• Help adapt PayPal’s advanced proprietary fraud-prevention tools—combining them with custom data and AI/ML techniques—to enable continued business growth.

• Provide 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.

What You’ll Bring:

• 2–4 years working with large-scale, complex datasets—ideally within fraud-risk, financial services, or a similarly regulated environment.

• 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.

• Expertise in 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.

Our Benefits:

Any general requests for consideration of your skills, please