Portfolio Analytics & Strategy Specialist (Card Fraud Strategy)
Position Overview
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.
Job Description
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.
Core Responsibilities
- 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
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
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 findings, recommendations, and business impacts to managers, directors, and senior leaders.
- Experience mentoring or providing technical guidance to junior analysts.
- Experience contributing to authorization strategy, fraud detection strategy, fraud monitoring, or fraud loss mitigation programs.
- Background working with Credit Card or Debit Card issuers, Consumer Banking organizations, Credit Unions, Payment companies, or Financial Services institutions.
- Experience partnering with external fraud vendors and third-party solution providers.
- Familiarity with card transaction ecosystems, payment processing, and fraud prevention technologies.
- Prior experience within large-scale card issuer environments such as national banks, card services organizations, or enterprise fraud organizations.
PNC Employees take pride in our reputation and to continue building upon that we expect our employees to be:
• Customer Focused
- Knowledgeable of the values and practices that align customer needs and satisfaction as primary considerations in all business decisions and able to leverage that information in creating customized customer solutions.
•
Managing Risk
- Assessing and effectively managing all of the risks associated with their business objectives and activities to ensure they adhere to and support PNC's Enterprise Risk Management Framework.
Qualifications
Not included in the source posting: about the role, what you'll do, benefits.
Skills
Who can apply
The employer didn't state any visa, work authorization, citizenship or clearance requirements in this posting. Confirm with the employer before applying.
Read automatically from the employer's posting text. Always confirm with the employer — requirements can change after a job is published.