PayPal Manager Data Science - Dublin 

Ireland, Dublin 

557528844

What you need to know about the role:is a Dublin-based role. Please note that you will need to have the appropriate right-to-work, and no relocation costs will be provided for this role.

Job Description:

In this role, you will own an end-to-end portfolio of PayPal products or markets, driving accountability for loss and decline rates while managing a small team of Data Scientists (3–6). You’ll collaborate with multiple stakeholders to design holistic fraud-prevention strategies, from initial data exploration through model deployment and performance monitoring. By striking a balance between speed and quality, you’ll ensure that our fraud-risk function makes a meaningful contribution to business growth, meeting key KPIs such as authentication rate targets and overall loss-rate objectives.

Day-to-Day Responsibilities

• Portfolio Ownership: Lead a group of Data Scientists to manage fraud and loss metrics for assigned product lines or markets.

• Strategy Development: Collaborate cross-functionally to develop and iterate on fraud-prevention frameworks that optimize transaction declines and minimize customer friction.

• Model Lifecycle Management: Guide the team in data preparation, feature engineering, model training, andvalidation—leveragingPython/R and cloud-native platforms (e.g., BigQuery, Snowflake).

• Stakeholder Alignment: Host regular syncs with Business Units, Risk Operations, and Compliance to ensure models and business rules remain aligned with evolving policy and regulatory requirements.

• Performance Monitoring: Track model health (drift,false-positive/false-negativerates) and lead root-cause analyses for any anomalies or spikes in loss.

What You’ll Bring

• 8+ years of relevant experience working with large-scale, complex datasets, including at least 3 years managing a team of 3–6 Data Scientists in a fraud-risk or financial-services environment.

• Proven ability to decompose ambiguous business requirements into a structured analytic plan and deliver data-driven recommendations.

• Advanced proficiency in SQL and Python (or R) for data wrangling, EDA, and model development.

• Expertise in exploratory data analysis and preparing clean, structured datasets for modelling

• Hands-on experience applying supervised and unsupervised learning techniques (regression, classification, clustering, decision trees, anomaly detection).

• Familiarity with production ML frameworks (scikit-learn, TensorFlow, PyTorch) and cloud data platforms (BigQuery, Snowflake).

• Deep understanding of fraud-risk principles, including AML/KYC, regulatory compliance, and performance metrics (Precision, Recall, ROC-AUC).

• Strong track record of partnering with engineers, product managers, and business leaders.

• Excellent verbal and written communication skills, able to distill complex findings into clear narratives for both technical and non-technical audiences.

• A passion for inventing new approaches to big, ambiguous problems—always looking to build novel solutions that stay ahead of evolving threat vectors.

Our Benefits:

Any general requests for consideration of your skills, please