Data Scientist Senior - Internal Audit

PNC; Chicago (IL); On-site; USD 140,000 - 190,000; Full time

Job summary

PNC's Internal Audit seeks a Senior Data Scientist to deliver advanced analytics, ML and AI-enabled solutions that enhance audit effectiveness and risk insight. You will be based in Cleveland, OH; Pittsburgh, PA; Raleigh, NC or Birmingham, AL, collaborating with audit, business, and technology teams to deploy scalable analytics in production.

You will mentor colleagues, translate complex findings into actionable insights, and help evolve the data science and AI capabilities within Internal Audit

Qualifications

• Advanced proficiency in Python and SQL.
• Experience with Pandas, NumPy, Scikit-learn, PySpark, TensorFlow or similar.
• Experience with ML, NLP, and AI-enabled analytics.
• Experience deploying production analytics in large-scale data environments.

Responsibilities

• Lead design, development, testing, deployment, and maintenance of analytics and AI-enabled solutions for Internal Audit.
• Translate business challenges into analytical data products with stakeholders.
• Develop predictive, diagnostic, anomaly detection, and risk-focused models.
• Build scalable analytics frameworks and data products across the audit lifecycle.
• Collaborate on model deployment, data pipelines, and governance compliance.
• Mentor team members and promote best practices in data science and analytics engineering.

Skills

Python

SQL

Pandas

NumPy

Scikit-learn

PySpark

TensorFlow

NLP

ML

data visualization

Tools

Hadoop

GitHub

Artifactory

Conda

pip

Power BI

Tableau

Job description

Position Overview

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 Scientist Senior within PNC's Internal Audit organization, you will be based in Cleveland, OH; Pittsburgh, PA; Raleigh, NC or Birmingham, AL. PNC's Internal Audit organization is seeking a highly motivated Senior Data Scientist to join a dynamic team that is redefining how risk is identified, monitored, and assessed across the enterprise. PNC's Internal Audit department is composed of industry leaders who provide independent, objective assurance and consulting services designed to add value and improve organizational performance. Through innovative use of data, emerging technologies, and analytical methodologies, the team is evolving audit practices to deliver deeper insights, proactive risk identification, and more effective assurance. As a Senior Data Scientist, you will play a key role in delivering advanced analytical solutions that enhance audit effectiveness, improve operational insights, and support strategic decision-making across Internal Audit. You will collaborate with audit professionals, business partners, and technology teams to develop scalable, production-ready analytics and AI-enabled solutions that help identify risk and drive value. The successful candidate will be a technically strong and innovative data scientist who can combine advanced analytical techniques with business acumen to solve complex risk challenges. This individual will help Internal Audit move beyond descriptive reporting toward predictive and AI-enabled insights that improve audit quality, strengthen risk identification, and drive strategic value across the organization.

Key Responsibilities

• Lead the design, development, testing, deployment, and maintenance of advanced analytics, machine learning, and AI-enabled solutions supporting Internal Audit priorities.
• Partner with audit teams and stakeholders to translate business challenges, audit objectives, and risk considerations into analytical solutions and data products.
• Develop predictive, diagnostic, anomaly detection, and risk-focused models that improve audit planning, testing, continuous monitoring, and reporting capabilities.
• Build scalable, maintainable, and reusable analytical frameworks, workflows, and data products that drive efficiency and insight generation across the audit lifecycle.
• Identify opportunities to leverage automation, machine learning, and generative AI technologies to enhance audit processes and risk identification.
• Collaborate with technology, data, and enterprise partners to support model deployment, productionization, data pipeline development, and ongoing solution maintenance.
• Ensure analytical solutions comply with established governance, validation, testing, documentation, and lifecycle management standards.
• Communicate analytical results and strategic recommendations to both technical and non-technical audiences, translating complex findings into actionable business insights.
• Mentor and provide technical guidance to team members while promoting best practices in data science, machine learning, software development, and analytics engineering.
• Contribute to the evolution of Internal Audit's data science, automation, and AI capabilities through innovation, thought leadership, and continuous improvement initiatives.

Desired Skills & Experience

Technical Skills

• Advanced proficiency in Python and SQL.
• Experience with data manipulation and analytical libraries such as Pandas, NumPy, Scikit-learn, PySpark, TensorFlow, or similar technologies.
• Experience working with large-scale data environments, including Hadoop-based platforms, distributed data processing frameworks, and enterprise data ecosystems.
• Experience using GitHub or other source control platforms for code management, collaboration, version control, and code review.
• Experience with machine learning, statistical modeling, predictive analytics, and data science methodologies.
• Experience with natural language processing (NLP), text analytics, document intelligence, topic modeling, and analysis of unstructured data.
• Experience developing, validating, deploying, and monitoring production analytics or machine learning solutions.
• Familiarity with software package, dependency, and artifact management tools such as Artifactory, Conda, pip, or similar enterprise development technologies.
• Familiarity with data visualization platforms such as Power BI, Tableau, or