Data Science Expert - Emerging Fraud Risk Lead

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(n) [position title] within PNC's [name of division] organization, you will be based in [city/state location of position]. Job Profile 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 Data Science Expert - Emerging Fraud Risk Lead within PNC's Enterprise Fraud organization, you can be based in Birmingham, AL; Phoenix, AZ; Lakewood, CO; Strongsville, OH; Cleveland, OH; Pittsburgh, PA; or 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 The Data Science Expert - Emerging Fraud Risk Lead is a highly visible individual contributor responsible for identifying and mitigating emerging fraud risks before they become significant business impacts. This role will initially focus on Credit Card and Lending fraud and will serve as a critical bridge between Fraud Monitoring, Incident Response, Countermeasures Analytics, and Fraud Strategy teams. Core Responsibilities Establish and mature the Emerging Fraud Risk capability, creating a scalable and repeatable pipeline of fraud detection and mitigation opportunities. Proactively identify emerging fraud threats, attack patterns, and vulnerabilities across Credit Card and Lending products before they result in significant losses. Apply advanced analytics, machine learning, anomaly detection, behavioral modeling, network analysis, and other data science techniques to uncover evolving fraud risks and validate risk hypotheses. Conduct complex investigations into emerging fraud behaviors, translating analytical findings into actionable recommendations and control opportunities. Serve as the bridge between portfolio monitoring and incident response teams, ensuring early risk indicators are prioritized and addressed proactively. Partner with Fraud Strategy, Countermeasures Analytics, Monitoring, Risk Management, Product, and Technology teams to develop and advance fraud mitigation solutions. Assess, design, and recommend detection strategies, fraud signals, and control enhancements for operational implementation. Lead end-to-end analytical initiatives utilizing large-scale structured and unstructured data to generate business insights and improve fraud prevention capabilities. Provide subject matter expertise in fraud analytics, machine learning, and risk modeling while establishing best practices for experimentation, model validation, and detection development. Influence strategic priorities and investment decisions through data-driven insights, risk assessments, and executive-level presentations. Evaluate emerging analytical methodologies and technologies to enhance fraud detection effectiveness and strengthen the enterprise fraud risk framework. Contribute thought leadership and drive innovation in fraud analytics and emerging risk management across the organization. Preferred Skills & Experience Experience in fraud analytics, data science, machine learning, or risk management, preferable within Credit Card, Lending, or Financial Services. Experience using Spark/PySpark to process large-scale datasets and develop scalable analytical and machine learning solutions in a production environment. Advanced experience using SQL, Python, and data science libraries to extract, analyze, and model large datasets in support of fraud detection and risk management initiatives. Hands-on experience developing, testing, and validating machine learning models using tools such as Scikit-learn, XGBoost, LightGBM, Pandas, and NumPy, or comparable technologies. Strong foundation in statistics, experimentation, and model validation. Proven ability to develop and deploy machine learning models and analytical solutions that identify #J-18808-Ljbffr