Data Scientist Lead - Data, Modeling & Analtyics

PNC Bank•Pittsburgh, PA

2d•Onsite

About The Position

This Data Science Lead role focuses on delivering decision ready insight through Descriptive (“what happened?”) and Diagnostic (“why did it happen?”) Analytics, with concrete outputs such as executive dashboards, KPI frameworks, trend and cohort analyses, drill downs, segmentation, driver analysis, and root cause investigations. The role is accountable for translating complex data into clear performance narratives by defining standardized metrics, ensuring data quality and lineage, and embedding analyses into scalable BI assets rather than ad hoc reporting. As needed, the role performs Prescriptive Analytics (“what should we do?”) by turning findings into decision recommendations through scenario analysis, sensitivity testing, and practical optimization or decision models, and by operationalizing insights into dashboards, alerts, and playbooks. Predictive Analytics (“what will happen?”) is in scope but not required, with forecasting, risk or propensity scoring, and applied machine learning viewed as nice to have capabilities. This role leads and prioritizes an analytics roadmap, coaches analysts and data scientists, and promotes disciplined use of tools such as SQL, Python, modern BI platforms, cloud data technologies, Git, and applied statistics. Strong partnership with product, operations, risk, and business leaders is essential to frame the right questions and deliver executive ready storytelling that connects analysis to action. The role enforces data governance, documentation, and audit ready practices, ensuring metric consistency and trusted reporting across teams. An experiment and measurement mindset is expected, applying test and learn or A/B testing to evaluate impact. Any models or advanced techniques used are monitored for performance, reliability, and fairness, with assumptions and limitations communicated transparently.

Requirements

Proven experience delivering Descriptive and Diagnostic Analytics, including KPI frameworks, executive dashboards, trend/cohort analysis, segmentation, driver analysis, and root cause investigations that inform business decisions.
Strong capability to translate analytical findings into Prescriptive Analytics, such as clear decision recommendations, scenario and sensitivity analysis, and practical optimization or decision models embedded into dashboards, alerts, or playbooks.
Demonstrated delivery leadership, including owning and prioritizing an analytics roadmap, coaching analysts and data scientists, setting analytical standards, and ensuring consistent, high impact outcomes.
Hands on proficiency with SQL, Python, BI visualization tools e.g., Tableau & Plotly, cloud data platforms, Git, and applied statistics, along with a strong foundation in data quality, metric definition, governance, documentation, and experimental or test and learn approaches.
Analytical Thinking
Competitive Advantages
Data Analytics
Data Mining
Data Science
Machine Learning (ML)
Data Architecture
Data Mining
Disruptive Innovation
Information Capture
Machine Learning
Modeling: Data, Process, Events, Objects
Prototyping
Query and Database Access Tools
Masters

Nice To Haves

Predictive Analytics (“what will happen?”) is in scope but not required, with forecasting, risk or propensity scoring, and applied machine learning viewed as nice to have capabilities.

Responsibilities

Directs and consults on analytical projects that leverage vast amounts of structured and unstructured data to extract actionable business insights.
Directs the data gathering, data processing and data mining of large and complex datasets.
Provides deep technical expertise to advance the data science capabilities of analytics teams.
Serves as a representative of PNC; understands the needs and challenges PNC is facing and provides thought leadership around data science activities both internally and externally.
Supervises the implementation of advanced analytics projects, and as an expert in the field, helps other data scientists present the analytical processes and outcomes to management.
Partners with Data Architects, Data Analysts, Data Engineers and Visualization Experts to develop data-driven solutions for the business.
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.