Data Science Expert - Emerging Fraud Risk Lead

PNC•Strongsville, OH

4d•$133,000 - $217,700•Onsite

About The Position

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.

Requirements

Experience in fraud analytics, data science, machine learning, or risk management, preferably 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 emerging risks and drive measurable business outcomes.
Experience applying advanced analytical techniques, including anomaly detection, predictive modeling, clustering, behavioral analytics, and network/graph analysis to uncover fraud patterns and validate risk hypotheses.
Strong analytical and problem-solving skills, with experience investigating complex fraud trends and translating findings into actionable recommendations.
Demonstrated leadership through influence, including leading analytical initiatives and driving results across cross-functional teams without direct management responsibility.
Strong communication and stakeholder management skills, with the ability to influence technical and business audiences and present recommendations to senior leaders.
Experience partnering across fraud, risk, strategy, business, and technology teams to operationalize data-driven solutions and fraud controls.
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.
Analytical Thinking, Competitive Advantages, Data Analytics, Data Mining, Data Science, Machine Learning, Machine Learning (ML), PySpark, Python (Programming Language), Statistics
Data Architecture, Data Mining, Disruptive Innovation, Information Capture, Machine Learning, Modeling: Data, Process, Events, Objects, Prototyping, Query and Database Access Tools
Roles at this level typically require a university / college degree. Higher level education such as a Masters degree, or PhD is desirable. Industry experience is typically 8 + years. Specific certifications are often required. In lieu of a degree, a comparable combination of education, job specific certification(s), and experience (including military service) may be considered.

Nice To Haves

Masters degree
PhD

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.