Quantitative Analytics & Model Analyst Senior - Credit Loss Forecasting (CLF)

PNC Bank•Pittsburgh, PA

1d•Onsite

About The Position

PNC is seeking a Quantitative Analytics & Model Analyst Senior for its Credit Loss Forecasting organization. The role is based in Pittsburgh, PA; Cleveland, OH; Tysons Corner, VA; Washington, DC; East Brunswick, NJ; or Charlotte, NC. PNC describes itself as an in-office company and encourages candidates to discuss workplace expectations with their recruiter and hiring manager. The analyst contributes to the design, development, implementation, and governance of quantitative analytics, model implementation, and AI-enabled automation supporting CECL, CCAR, model monitoring, and other strategic risk initiatives. The position requires hands-on Python software development, quantitative and data-engineering capabilities, and experience delivering reliable analytical solutions in enterprise environments. The analyst works across the solution lifecycle, translating business and risk requirements into scalable capabilities and applying appropriate controls and testing standards. The role partners with modeling, technology, validation, risk, and business stakeholders, and may evaluate machine-learning and generative-AI capabilities when they offer business value and can be used with suitable security, explainability, governance, and human oversight. The position also performs advanced quantitative analyses and model development to inform decisions, makes recommendations based on analysis, supports lines of business with new model frameworks, and refines, monitors, and reviews existing models. Work includes assessing models’ theoretical foundations, design, implementation, and data quality and integrity; analyzing complex datasets and model risks; and preparing documentation for validation and regulatory compliance. PNC does not provide employment visa sponsorship or participate in STEM OPT for this position. The role is incentive eligible, with compensation based on company, business, and/or individual performance. The job posting states that it is generally expected to remain open for two business days from 11/06/2026, though it may remain open longer at the company’s discretion.

Requirements

Bachelor’s or master’s degree in quantitative finance, statistics, mathematics, computer science, data science, engineering, operations research, or a related quantitative field; an advanced degree is preferred.
Solid experience in quantitative analytics, model implementation, model monitoring, or AI/ML solutions in enterprise environments, including onboarding, testing, output validation, production deployment, and issue remediation.
Strong hands-on Python programming experience building modular, testable, production-ready solutions, including automated workflows, data pipelines, controls, and reconciliation processes using large, complex datasets.
Solid knowledge of statistical modeling, machine learning, and quantitative methods.
Demonstrated ability to diagnose and resolve issues involving code, data, workflows, and analytical outputs, applying standards for testing, documentation, version control, and change management.
Understanding of model risk, data privacy, information security, responsible AI, and governance requirements.
Strong communication and collaboration skills, ability to manage multiple priorities, and ability to explain technical concepts to technical and non-technical audiences.
Roles at this level typically require a university or college degree and 3 or more years of relevant or direct industry experience. A comparable combination of education, job-specific certifications, and experience, including military service, may be considered in lieu of a degree.
Must demonstrate appropriate knowledge, skills, and abilities for the position.

Nice To Haves

Advanced degree in a relevant quantitative field.
Hands-on experience with PySpark, SQL, Git/Bitbucket, APIs, Parquet and CSV data, Slurm or comparable distributed job scheduling, enterprise model-execution platforms, and workflow orchestration.
Experience with quantitative models in financial services, particularly credit risk, CECL, CCAR, stress testing, or model governance, and knowledge of banking products and regulatory expectations in highly regulated environments.
Experience applying AI/ML or generative-AI/LLM solutions to analytical or model workflows, including agentic workflows, retrieval-augmented generation, vector databases, evaluation, and guardrails.
Experience participating in design and code reviews and mentoring junior team members.
Certifications are often desired.

Responsibilities

Contribute to the design, development, implementation, and enhancement of quantitative analytics, model-support, and AI-enabled automation solutions for CECL, CCAR, model monitoring, and other regulatory or business-critical initiatives.
Provide hands-on support through requirements gathering, design, controlled deployment, and production support.
Develop and review modular, testable, production-ready Python and PySpark code, data pipelines, controls, and workflow automation.
Apply established standards for code quality, version control, testing, documentation, security, and change management.
Support model onboarding, implementation, monitoring, and remediation.
Develop and execute unit, integration, regression, reconciliation, and performance tests to verify implementation accuracy and operational readiness.
Diagnose and resolve issues involving model execution, data quality, workflow orchestration, configuration, and analytical outputs; analyze complex datasets to assess model performance and investigate discrepancies; escalate complex issues when appropriate.
Evaluate and apply generative-AI and LLM-based capabilities when appropriate to improve analytics, model operations, monitoring, and documentation, using suitable testing, security, human oversight, and governance controls.
Support model and analytical governance, including risk assessments, validation support, audit and regulatory responses, and issue remediation.
Prepare clear, supportable technical and governance documentation, including detailed documents for validation and regulatory compliance.
Partner with model owners, developers, validation, technology, and business teams; participate in design and code reviews; and communicate findings and recommendations to technical and non-technical stakeholders.
Perform advanced quantitative analyses and model development to support decision-making, run quantitative strategies, and make recommendations based on findings.
Develop model frameworks in support of lines of business and refine, monitor, and review existing models.
Assess theoretical aspects, model design and implementation, and data quality and integrity; use quantitative tools to measure model risks and determine model strengths and limitations.
Maintain ongoing communication with model owners and developers during model reviews and produce complex reports and analyses.

Benefits

Medical and prescription drug coverage, with a Health Savings Account feature
Dental insurance
Vision insurance
Employee and spouse/child life insurance
Short- and long-term disability protection
401(k) with PNC match
Pension plan
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
Up to 11 paid holidays each year
9 occasional absence days each year, unless otherwise required by law
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