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Data Scientist - Anti-Money Laundering Analytics & Modeling - PNC Financial Services Group
Data Scientist
Data Scientist focused on anti‑money laundering analytics, building predictive models and detection workflows using Python, SQL, machine‑learning techniques, and data‑visualization tools to identify suspicious activity and support compliance initiatives.
About the role
Key Responsibilities
- Design, develop, and deploy machine‑learning models to detect suspicious transactions and support AML investigations.
- Analyze large, heterogeneous data sets (transactional, customer, external) to uncover patterns of illicit behavior.
- Collaborate with compliance analysts, engineers, and risk managers to translate business requirements into analytical solutions.
- Implement statistical and predictive modeling techniques, evaluate model performance, and maintain model governance documentation.
- Create dashboards and visual reports that communicate risk insights to stakeholders and regulatory bodies.
Requirements
- Strong proficiency in Python (pandas, scikit‑learn, PySpark) and SQL for data extraction and manipulation.
- Experience building and validating machine‑learning or statistical models for fraud, AML, or related risk domains.
- Solid understanding of AML regulations, transaction monitoring, and risk‑based scoring methodologies.
- Ability to translate complex analytical results into clear visualizations and actionable recommendations.
- Graduate degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics) or equivalent professional experience.
Skills
pythonsqlmachine learning