Data Scientist Senior - Internal Audit

PNC Bank•Pittsburgh, PA

12d•Onsite

About The Position

PNC is seeking a Senior Data Scientist in its Internal Audit organization to develop advanced analytical, machine learning, and AI-enabled solutions that improve audit effectiveness, operational insights, risk identification, and strategic decision-making. The role collaborates with audit professionals, business partners, technology teams, and enterprise data partners to turn business challenges, audit objectives, and risk considerations into scalable, production-ready analytics and data products. The position is based in Cleveland, Ohio; Pittsburgh, Pennsylvania; Raleigh, North Carolina; or Birmingham, Alabama, and PNC describes itself as an in-office company. The Senior Data Scientist leads analytical projects using structured and unstructured data, directs data gathering and processing, and develops models and algorithms to predict outcomes and recommend actions. The role supports audit planning, testing, continuous monitoring, and reporting through predictive, diagnostic, anomaly-detection, and risk-focused methods. It also contributes to solution deployment and maintenance, governance and model lifecycle practices, stakeholder communication, technical mentorship, and continued development of Internal Audit’s data science, automation, and AI capabilities. The position typically requires a university or college degree and five or more years of industry-relevant experience. A comparable combination of education, job-specific certifications, and experience may be considered in place of a degree. A master’s degree is listed under Education. No certification or license is required. The position is incentive eligible, with compensation based in part on company, business, and/or individual performance.

Requirements

Advanced proficiency in Python and SQL.
Experience with data manipulation and analytical libraries such as Pandas, NumPy, Scikit-learn, PySpark, or TensorFlow, or similar technologies.
Experience 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, text analytics, document intelligence, topic modeling, and unstructured data analysis.
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, or pip, or similar enterprise development technologies.
Familiarity with data visualization platforms such as Power BI or Tableau, or similar tools.
Strong problem-solving and critical-thinking capabilities.
Ability to influence stakeholders and communicate effectively across technical and business audiences.
Ability to manage multiple priorities and deliver high-quality solutions in a dynamic environment.
Strong collaboration skills and interest in mentoring and developing others.
Typically, a university or college degree and five or more years of industry-relevant experience; a comparable combination of education, job-specific certifications, and experience may be considered in lieu of a degree.
Customer-focused approach, including the ability to use knowledge of customer values and needs in business decisions and customized solutions.
Ability to assess and manage risks in accordance with PNC’s Enterprise Risk Management Framework.

Nice To Haves

Experience with generative AI, large language models, prompt engineering, retrieval-augmented generation, AI agents, or related technologies.
Understanding of AI governance, model risk management, and responsible AI practices.
Preferred skills include analytical thinking, competitive advantages, data analytics, data mining, data science, and machine learning.

Responsibilities

Lead the design, development, testing, deployment, and maintenance of advanced analytics, machine learning, and AI-enabled solutions for Internal Audit.
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 for audit planning, testing, continuous monitoring, and reporting.
Build scalable, maintainable, and reusable analytical frameworks, workflows, and data products across the audit lifecycle.
Identify opportunities to use automation, machine learning, and generative AI to enhance audit processes and risk identification.
Collaborate with technology, data, and enterprise partners on model deployment, productionization, data pipelines, and ongoing solution maintenance.
Ensure analytical solutions follow governance, validation, testing, documentation, and lifecycle management standards.
Communicate analytical results and strategic recommendations to technical and non-technical audiences, translating findings into actionable business insights.
Mentor and provide technical guidance to team members and promote 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.
Lead analytical projects using structured and unstructured data, including data gathering, processing, and mining of large and complex datasets.
Develop algorithms using advanced mathematical and statistical techniques to predict business outcomes and recommend actions to management.
Conduct analytical experiments to identify product and process optimization opportunities.
Present business insights to management using visualization technologies and data storytelling.
Partner with Data Architects, Data Analysts, Data Engineers, and Visualization Experts to develop data-driven business solutions.
Assess and manage risks associated with business objectives and activities in support of PNC’s Enterprise Risk Management Framework.

Benefits

Medical and prescription drug coverage, with a Health Savings Account feature, for eligible full-time employees.
Dental and vision coverage options.
Employee and spouse/child life insurance.
Short- and long-term disability protection.
401(k) with PNC match.
Pension and stock purchase plans.
Dependent care reimbursement account.
Back-up child and elder care.
Adoption, surrogacy, and doula reimbursement.
Educational assistance, including select fully paid programs.
Wellness program with financial incentives.
Maternity and/or parental leave, depending on eligibility.
Up to 11 paid holidays each year, depending on eligibility.
Nine occasional absence days each year, unless otherwise required by law, depending on eligibility.
15 to 25 vacation days each year, depending on career level and years of service.
Incentive-eligible compensation based on company, business, and/or individual performance.