Data Science Lead & Vp, Strategy & Insights

Jpmorgan Chase; Plano (TX); On-site; USD 133,000 - 218,000; Full time

Job summary

PNC seeks a Data Science Expert - Emerging Fraud Risk Lead to identify and mitigate emerging fraud risks in Credit Card and Lending. You will bridge Fraud Monitoring, Incident Response, and Countermeasures Analytics, driving scalable detection and proactive mitigation initiatives.

The role emphasizes advanced analytics, ML, and collaboration with Fraud Strategy, Risk Management and Technology teams to strengthen enterprise fraud controls.

Qualifications

• Experience in fraud analytics, data science, machine learning, or risk management within financial services.
• Proficient in Spark/PySpark for large-scale data processing and production ML solutions.
• Advanced SQL, Python and data science libraries for fraud detection and risk management.
• Hands-on ML development using Scikit-learn, XGBoost, LightGBM, Pandas and NumPy.

Responsibilities

• Establish and mature the Emerging Fraud Risk capability with scalable detection pipelines.
• Identify emerging fraud threats across Credit Card and Lending products.
• Apply advanced analytics and ML techniques to uncover evolving fraud risks.
• Investigate complex fraud behaviors and translate findings into actionable recommendations.
• Bridge portfolio monitoring and incident response, prioritizing early risk indicators.
• Collaborate with Fraud Strategy, Countermeasures Analytics, Monitoring and Technology teams.
• Lead end-to-end analytical initiatives using large structured and unstructured data.
• Provide SME insights in fraud analytics, ML, and risk modeling.
• Influence executive-level decisions with data-driven risk assessments.
• Evaluate new analytic methods to enhance detection and enterprise risk framework.
• Drive thought leadership in fraud analytics and emerging risk management.

Skills

Fraud analytics

Data science

Machine learning

SQL

Python

PySpark

Statistical modeling

Communication

Stakeholder management

Education

Masters

Tools

Scikit-learn

XGBoost

LightGBM

Pandas

NumPy

Spark

Job description

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 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, 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.

Qualifications

Successful candidates must demonstrate appropriate knowledge, skills, and abilities for a role. Listed below are skills, competencies, work experience, education, and required certifications/licensures needed to be successful in this position.

Preferred Skills

• Analytical Thinking, Competitive Advantages, Data Analytics, Data Mining, Data Science, Machine Learning, Machine Learning (ML), PySpark, Python (Programming Language), Statistics

Competencies

Data Architecture, Data Mining, Disruptive Innovation, Information Capture, Machine Learning, Modeling: Data, Process, Events, Objects, Prototyping, Query and Database Access Tools

Work Experience

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.

Education

Masters

Certifications

No Required Certification(s)

Licenses

No Required License(s)

Language Assessments

No Required or Preferred Language Assessments

Pay Transparency

Base Salary: $133,000.00 - $217,700.00. Salaries may vary based on geographic location, market data and on individual skills, experience, and