AI Governance Lead, Data Science & Testing Manager

PNC; Pittsburgh (Allegheny County); On-site; USD 123,000 - 252,000; Full time

Job summary

PNC is seeking a Data Scientist Manager Sr to lead AI governance and testing within the Data & Automation team. Based in Pittsburgh, Strongsville, Dallas, or Birmingham, you will drive enterprise AI risk management, establish performance and safety standards, and mentor a team of AI specialists.

You will shape testing methodologies, contribute to technical strategy, and collaborate with Model Risk Management to ensure responsible AI across pre- and post-deployment stages.

Qualifications

• University/college degree required; higher education such as Masters or PhD desirable.
• Industry experience typically 8+ years.
• At least 5 years of prior management experience.

Responsibilities

• Oversees end-to-end analytical projects leveraging vast amounts of structured and unstructured data to extract actionable business insights.
• Provides leadership, direction and methodology for Data Scientists to create appropriate models and data-driven solutions for the business.
• Recruits, trains, and develops best-in-class Data Scientists.
• Works with executive leadership to identify data science project opportunities and assists in planning activities including budget and scope determination.
• Oversees relationship with Data Architects, Data Analysts, Data Engineers and Visualization Experts to develop data-driven solutions for the business.

Skills

AI Governance

Analytical Thinking

Artificial Intelligence (AI)

Competitive Advantages

Data Analytics

Data Mining

Data Science

Machine Learning (ML)

Responsible AI

Education

Master's degree

Job description

PNC is seeking a Data Scientist Manager Sr to lead AI governance and testing within the Data & Automation team. Based in Pittsburgh, Strongsville, Dallas, or Birmingham, you will drive enterprise AI risk management, establish performance and safety standards, and mentor a team of AI specialists.

You will shape testing methodologies, contribute to technical strategy, and collaborate with Model Risk Management to ensure responsible AI across pre- and post-deployment stages.