Quantitative Analytics & Model Analyst Senior - C&IB - Commercial

Quantitative Analytics & Model Analyst Senior

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 Quantitative Analytics & Model Analyst Senior within PNC's Data Model Analytics organization, you will be based in Pittsburgh or Philadelphia, PA; Cleveland, OH; or Tyson's Corner, VA.

Key Responsibilities:

• Develop, implement, and maintain quantitative and machine learning models for analytics and decision support.
• Apply statistical and mathematical techniques, including logistic and linear regression, to solve business problems.
• Perform data analysis, feature engineering, model training, testing, and validation.
• Use Python to write clean, efficient, and well documented code for modeling and analysis.
• Evaluate model performance using appropriate metrics and ensure robustness and accuracy.
• Prepare presentations and written documentation to clearly communicate model methodology, results, and insights to technical and non technical audiences.
• Collaborate with cross functional partners to understand requirements and translate them into analytical solutions.
• Support model lifecycle activities, including enhancements, recalibration, and monitoring.

Required Experience & Skills:

2–3+ years of professional experience in quantitative analytics, modeling, or a related role. Strong Python programming and coding skills. Solid foundation in mathematics and statistics. Hands on experience with logistic regression and linear regression. Experience applying machine learning techniques. Proven experience developing and implementing models end to end. Ability to build or demonstrate a portfolio of modeling or analytical work. Bachelor's degree in mathematics, Statistics, Quantitative Analytics, or a related quantitative field. Strong presentation, communication, and documentation skills, with the ability to explain complex concepts clearly.

Preferred Experience & Skills:

Experience with PySpark or working in distributed data environments. Banking or financial services experience. Knowledge of credit risk or portfolio metrics such as PD (Probability of Default), LGD (Loss Given Default), and EAD/ED (Exposure at Default).