Quantitative Modeling & Analytics Specialist

PNC; Arkansas; On-site; USD 75,000 - 150,000; Full time

Job summary

PNC is seeking a Quantitative Analytics & Model Analyst within the Retail Lending Data Modeling & Analytics organization. You will be based in Pittsburgh, PA; Cleveland, OH; or Tyson’s Corner, VA, focusing on developing and validating quantitative models for retail lending analytics.

You will apply reinforcement learning and optimization techniques, analyze model interactions with systems, and collaborate with engineering teams to productionize models while ensuring robust validation and

Qualifications

• 1–2 years of quantitative modeling, reinforcement learning, optimization modeling, or related analytical work.
• Experience or academic exposure to reinforcement learning and/or optimization techniques.
• Strong quantitative, mathematical, and statistical background.
• Understanding of human input models or unique modeling approaches.

Responsibilities

• Design, develop, and evaluate quantitative models used in retail lending analytics and optimization.
• Apply reinforcement learning and optimization techniques to decision-based problems.
• Analyze how models interact with computer systems, inputs, outputs, and feedback loops.
• Document model assumptions, methodologies, results, and limitations.
• Collaborate with technical teams to support model integration into production systems.

Skills

Quantitative modeling

Reinforcement learning

Optimization modeling

Human input models

Model deployment understanding

Education

Bachelor’s degree in Mathematics, Statistics, or related field

Job description

PNC is seeking a Quantitative Analytics & Model Analyst within the Retail Lending Data Modeling & Analytics organization. You will be based in Pittsburgh, PA; Cleveland, OH; or Tyson’s Corner, VA, focusing on developing and validating quantitative models for retail lending analytics.

You will apply reinforcement learning and optimization techniques, analyze model interactions with systems, and collaborate with engineering teams to productionize models while ensuring robust validation and