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

PNC•Washington, DC

1d•$86,250 - $172,500•Onsite

About The Position

At PNC, employees work together to deliver a strong customer experience and foster an inclusive workplace. The Quantitative Analytics & Model Analyst Senior works in PNC's Credit Loss Forecasting (CLF) organization and may be based in Pittsburgh, PA; Cleveland, OH; Tyson's Corner, VA; Washington, DC; East Brunswick, NJ; or Charlotte, NC. The role 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. It calls for strong Python software-development skills, quantitative and data-engineering capabilities, and experience delivering reliable analytical solutions in enterprise environments. The analyst contributes hands-on across the solution lifecycle, translating business and risk requirements into scalable capabilities and applying appropriate controls and testing standards while partnering with modeling, technology, validation, risk, and business teams. The position also evaluates and introduces machine learning and generative AI capabilities when they offer clear business value and can be implemented with appropriate security, explainability, governance, testing, and human oversight. The analyst independently performs advanced quantitative analyses and model development to support decision-making, refines and reviews models, assesses model risk and limitations, and prepares documentation for validation and regulatory compliance. PNC describes this as an in-office position. The role is incentive eligible. PNC will not provide employment visa sponsorship or participate in STEM OPT for this position.

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.
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, including the ability to manage multiple priorities and explain technical concepts to technical and non-technical audiences.
Typically, a university or college degree and 3 or more years of relevant or direct industry experience are required at this level; a comparable combination of education, job-specific certifications, and experience may be considered in lieu of a degree.
No required certifications or licenses.

Nice To Haves

Hands-on experience with PySpark, SQL, Git/Bitbucket, APIs, Parquet and CSV data, Slurm or comparable distributed job scheduling, and 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-based 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.
Advanced degree.
Knowledge or skills in analytical thinking, credit risks, data analytics, financial analysis, model development, operational risks, quantitative models, and risk appetite.
Competencies in bank quantitative analysis, consulting, data gathering and reporting, effective communications, predictive analytics, quantitative techniques, the financial-services regulatory environment, and testing.
Certifications are often desired.

Responsibilities

Contribute hands-on 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, from requirements and design through controlled deployment and production support.
Develop and review modular, testable, production-ready Python and PySpark code, data pipelines, controls, and workflow automation, following standards for code quality, version control, testing, documentation, security, and change management.
Support model onboarding, implementation, monitoring, and remediation, including executing unit, integration, regression, reconciliation, and performance testing.
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, escalating complex issues as appropriate.
Evaluate and apply generative AI and LLM-based capabilities to improve analytics, model operations, monitoring, and documentation, with appropriate testing, security, human oversight, and governance controls.
Support model and analytical governance through risk assessments, validation support, audit and regulatory responses, issue remediation, and preparation of clear technical and governance documentation.
Partner with model owners, development, validation, technology, and business teams; participate in design and code reviews; and communicate findings and recommendations to technical and non-technical stakeholders.
Independently perform advanced quantitative analyses and model development to inform decision-making, and make recommendations based on analyses.
Develop new model frameworks in support of the line of business; refine, monitor, and review existing models; and communicate with model owners and developers during reviews.
Perform quantitative analysis and develop complex reports; assess model theory, design, implementation, and data quality and integrity; and make recommendations based on data analytics.
Use quantitative tools and techniques to measure and analyze model risk and reach conclusions about model strengths and limitations.
Prepare and analyze detailed documents for validation and regulatory compliance using applicable templates.
Assess and manage risks associated with business objectives and activities in accordance with PNC's Enterprise Risk Management Framework.

Benefits

Medical and prescription drug coverage, with a Health Savings Account feature, subject to eligibility.
Dental and vision options.
Employee and spouse/child life insurance.
Short- and long-term disability protection.
401(k) with PNC match.
Pension and 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, depending on eligibility.
Up to 11 paid holidays each year, depending on eligibility.
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, subject to eligibility.