Portfolio Analytics & Strategy Specialist - Consumer Data Solutions & Analytics at PNC Financial Services in Pittsburgh, PA

Job description

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 Portfolio Analytics & Strategy Specialist within PNC's Consumer Data Solutions & Analytics organization, you will be based in Pittsburgh, or Philadelphia, PA; Cleveland, OH; Wilmington, DE; East Brunswick, NJ; or Tyson's Corner, VA. Job Overview: The Consumer Data Solutions & Analytics team is seeking a highly motivated and strategic Portfolio Analytics & Strategy Specialist to lead data-driven decision making, business insights generation, and analytical solution development across customer-focused initiatives. This role will leverage advanced analytics, business intelligence, and stakeholder engagement to drive business performance, risk management, regulatory compliance, and customer data strategy. The ideal candidate brings extensive experience in data analytics, business analytics, stakeholder management, and risk-focused environments, along with strong technical expertise in SQL, Python, and data visualization tools. Experience working with sandbox data environments is a strong plus. This individual will partner with business leaders, risk teams, technology teams, and compliance stakeholders to deliver actionable insights that support strategic business objectives. Key Responsibilities: • Lead complex analytics initiatives that support business strategy, customer experience, risk management, and operational effectiveness. • Analyze large and diverse consumer datasets to provide actionable business insights and recommendations. • Leverage sandbox environments to explore, analyze, validate, and transform large consumer datasets, enabling efficient development of analytical insights, reporting solutions, and business recommendations. • Partner with business stakeholders to identify opportunities, define analytical requirements, and prioritize initiatives. • Develop and maintain advanced reporting, dashboards, scorecards, and analytical solutions using Python, SQL, Tableau, and Power BI. • Communicate analytical findings and data-driven recommendations to senior leadership and executive stakeholders. • Drive business performance measurement through KPI development, trend analysis, forecasting, and performance monitoring. • Translate complex technical findings into clear, concise business recommendations for non-technical audiences. • Collaborate across Analytics, Technology, Product, Risk, Compliance, Governance, and Operations teams. • Support regulatory, governance, compliance, and risk management initiatives through data analysis and reporting. • Identify opportunities to automate reporting, improve data quality, and enhance decision-making processes. • Ensure alignment with enterprise data governance, regulatory requirements, and risk management frameworks. • Mentor junior analysts and provide guidance on analytical best practices and business intelligence solutions. Required Experience & Skills: • 5+ years of experience in: Data Analytics, Business Analytics, Business Intelligence, and/or Data-Driven Decision Support. • Strong experience presenting complex analytical findings to business and executive stakeholders. • Demonstrated success communicating with cross-functional teams and senior leadership. • Strong analytical thinking and problem-solving abilities. • Experience leveraging sandbox data environments to perform data exploration, validation, analysis, and solution development for business and risk management initiatives. • Experience supporting Risk Management, Governance, Compliance, or Regulatory-focused initiatives. • Advanced SQL expertise including: Complex Queries, Joins, Aggregations, Subqueries, Data Validation, and Performance Optimi