Senior Quant Analytics & Model Analyst – Credit Loss

PNC Financial Services Group, Inc.; Washington (District of Columbia); On-site; USD 86,000 - 173,000; Full time

Benefits offered by this job

Medical benefits

401(k) matching

Paid time off

Job summary

PNC Financial Services Group, Inc. is seeking a Senior Quantitative Analytics & Model Analyst to support CECL, CCAR, and Model Monitoring initiatives within the Credit Loss Forecasting team.

Based in multiple in-office locations including Washington, DC area, the role focuses on developing and governance of quantitative models with Python-based solutions. The ideal candidate has 3+ years in financial services modeling, strong Python skills, experience with large datasets, and the ability to

Qualifications

• Bachelor's or Master's degree in a quantitative field.
• Strong Python programming and software development experience.
• Experience with large-scale datasets and data manipulation frameworks.
• Strong problem-solving and collaboration skills.

Responsibilities

• Develop, maintain, and enhance analytical processes and infrastructure for CECL, CCAR, and Model Monitoring.
• Collaborate with model development and validation teams to support onboarding, testing, and deployment.
• Design and maintain Python-based solutions to automate data processing, analytics, controls and reporting.
• Troubleshoot complex issues related to model execution and data quality.
• Perform quantitative analysis on large datasets to identify trends and support decisions.

Skills

Python

Data analysis

Communication

Education

Bachelor's or Master's degree in Quantitative fields

Tools

PySpark

Git/Bitbucket

Job description

PNC Financial Services Group, Inc. is seeking a Senior Quantitative Analytics & Model Analyst to support CECL, CCAR, and Model Monitoring initiatives within the Credit Loss Forecasting team.

Based in multiple in-office locations including Washington, DC area, the role focuses on developing and governance of quantitative models with Python-based solutions. The ideal candidate has 3+ years in financial services modeling, strong Python skills, experience with large datasets, and the ability to