Portfolio Analytics & Strategy Specialist; Card Fraud Strategy

At PNC, our people are our greatest differentiator and competitive advantage in the markets we serve. We are all united in delivering the best experience for our customers. We work together each day to foster an inclusive workplace culture where all of our employees feel respected, valued and have an opportunity to contribute to the company's success. As a Fraud Data Scientist (Credit/Debit Card Transaction & Strategy) within PNC's Enterprise Fraud organization, you can be based in Birmingham, AL;
Phoenix, AZ;
Lakewood, CO;
Strongsville, OH;
Columbus, OH;
Philadelphia, PA;
Pittsburgh, PA;
Dallas, TX.

PNC is an in-office company that fosters a supportive culture where employees can thrive and achieve balance. We encourage candidates to connect with their recruiter and hiring manager to understand workplace expectations and ensure the role aligns with their goals.

PNC will not provide sponsorship for employment visas or participate in STEM OPT for this position.

We are seeking a Portfolio Analytics & Strategy Specialist to join our Fraud Strategy & Analytics team, supporting both Credit Card and Debit Card fraud mitigation initiatives.

As a senior member of the team, you will play a key role in developing and optimizing fraud prevention strategies that balance fraud loss reduction, customer experience, and business growth. You will analyze large and complex datasets, identify emerging fraud trends, recommend strategic countermeasures, and partner closely with cross-functional stakeholders across Product, Technology, Operations, Modeling, and Fraud Risk teams.

• Lead complex analytical initiatives focused on Credit Card and Debit Card fraud prevention and fraud mitigation strategies.
• Analyze large volumes of structured and unstructured data to identify fraud patterns, emerging threats, behavioral trends, and opportunities for optimization.
• Develop data-driven recommendations that balance fraud loss prevention with customer experience and business growth objectives.
• Design, evaluate, and optimize fraud rules, detection strategies, and monitor frameworks.
• Apply statistical modeling, predictive analytics, data mining, and advanced analytical techniques to support fraud decisioning and risk management efforts.
• Perform graph analysis, linkage analysis, data correlation, and visualization to uncover fraud networks and relationships.
• Collaborate with partners across Fraud, Operations, Technology, Product, Modeling, Legal, and other business functions to drive enterprise-wide solutions.
• Present findings, strategy recommendations, and performance results to management and senior leadership audiences.
• Translate complex analytical findings into actionable business insights for both technical and non-technical stakeholders.
• Support strategic initiatives by evaluating the downstream impact of fraud strategy changes on customers, operations, and business outcomes.
• Act as a senior team member and technical mentor, providing guidance and support to junior analysts.
• Participate in the development and enhancement of analytical frameworks, reporting methodologies, and fraud monitoring capabilities.
• Work in a fast-paced environment where timely analysis and decision-making directly impact business performance and fraud exposure.

• 5+ years of experience in Data Science
  , Business Analytics
  , Data Analytics
  , Fraud Analytics
  , Risk Analytics
  , Decision Science
  , or a related analytical field.
• 1+ year of experience supporting Credit Card or Debit Card fraud analytics
  , fraud strategy
  , or fraud risk management
  .
• Strong programming experience with Python for analytics, statistical analysis, predictive modeling, and data processing; experience with R is a plus.
• Advanced proficiency with SQL for querying, analyzing, and manipulating large datasets.
• Experience working with large-scale datasets and complex analytical environments.
• Strong background in statistical modeling
  , predictive analytics
  , data mining
  , and quantitative analysis
  .
• Experience leveraging Apache Spark for large-scale data processing and analytics.
• Knowledge of optimization frameworks and analytical techniques used to improve business outcomes.
• Experience with graph analysis, linkage analysis, network analysis, or entity relationship analysis.
• Strong data visualization and storytelling capabilities utilizing tools such as Microsoft Power BI or similar visualization platforms.
• Demonstrated ability to present analytical…