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PNC Financial Services

Runs a national bank for people and businesses

Quantitative Analytics & Model Analyst Senior - Credit Loss Forecasting

Quantitative RiskConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • A Bachelor's or Master's degree in Quantitative Finance, Statistics, Mathematics, Computer Science, Data Science, Engineering, Operations Research, or a related quantitative field is required; 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 is required.
  • 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 is required.
  • Solid knowledge of statistical modeling, machine learning, and quantitative methods is required.
  • The candidate must be able to diagnose and resolve issues involving code, data, workflows, and analytical outputs, applying standards for testing, documentation, version control, and change management.
  • The candidate must understand model risk, data privacy, information security, responsible AI, and governance requirements.
  • The candidate must be able to communicate technical concepts to technical and non-technical audiences and manage multiple priorities.
  • PNC will not provide sponsorship for employment visas or participate in STEM OPT for this position.

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, providing hands-on support 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, applying established standards for code quality, version control, testing, documentation, security, and change management.
  • Support model onboarding, implementation, monitoring, and remediation, including developing and executing unit, integration, regression, reconciliation, and performance testing to confirm implementation accuracy and operational readiness.
  • Diagnose and resolve issues involving model execution, data quality, workflow orchestration, configuration, and analytical outputs; analyze large, complex datasets to assess model performance and investigate discrepancies, escalating complex issues as appropriate.
  • Evaluate and apply generative AI and large language model-based capabilities where appropriate to improve analytics, model operations, monitoring, and documentation, with appropriate testing, security, human oversight, and governance controls.
  • Support model and analytical governance, including risk assessments, validation support, audit and regulatory responses, and issue remediation, and prepare clear, supportable 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 clearly to technical and non-technical stakeholders.
  • Perform advanced quantitative analyses and model development to drive decision-making by running quantitative strategies, and make recommendations based on analyses.
  • Analyze and develop new model frameworks by supporting the line of business; refine, monitor, and review existing models; communicate on an ongoing basis with model owners and developers during reviews; and work with larger, more complex datasets to create models.
  • Perform quantitative analysis and develop complex reports; conduct qualitative and quantitative assessments of model theory, design, implementation, data quality, and integrity; analyze complex data and associated quantitative analysis; and make recommendations based on findings.
  • Use quantitative tools and techniques to measure and analyze model risks and reach conclusions on model strengths and limitations.
  • Prepare and analyze detailed documents for validation and regulatory compliance using applicable templates.
  • Assess and effectively manage risks associated with business objectives and activities to support PNC's Enterprise Risk Management Framework.

Desired Qualifications

  • 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, along with 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.
  • Analytical thinking, credit risks, data analytics, financial analysis, model development, operational risks, quantitative models, and risk appetite.

About the Company

PNC Financial Services

Runs a national bank for people and businesses

PNC Financial Services runs one of the largest banks in America, serving households and businesses of every size through branches, apps, and corporate bankers. Acquisitions turned it from a regional lender into a coast-to-coast bank, notably its purchases of National City during the 2008 financial crisis and BBVA USA in 2021. Consumers keep checking and savings accounts, such as Virtual Wallet with its built-in budgeting tools, and take out mortgages and credit cards, while companies borrow and manage cash through PNC. It earns most of its money on the gap between loan interest and deposit costs, and the rest from fees. PNC traces its roots to a savings bank founded in 1845 and is based in Pittsburgh, Pennsylvania.

Company size
10,001+
Company stage
IPO
Headquarters
Pittsburgh, PA
Founded
1845

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