Data Scientist Jobs

About The Position

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.