Manager, Quantitative Consulting
Listed on 2026-07-26
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Finance & Banking
Risk Manager/Analyst, AI Evaluation
US-NC-Charlotte, US-NC-Charlotte, US-NY-New York
Working time
Full Time
Description & RequirementsWe are seeking a dynamic, client‑facing Quantitative Manager to join our Quantitative & Artificial Intelligence (AI) Solutions team. This role is designed for a well‑rounded quantitative manager who combines deep, hands‑on modeling expertise with the leadership and delivery discipline required to build, validate, govern, and run models in complex, highly regulated environments.
You will work with large, systemically important financial institutions and other complex banking organizations, partnering with senior stakeholders across Risk, Finance, Treasury, Compliance, and Technology to strengthen their model development, model validation, model risk management (MRM), and model operations capabilities in alignment with SR 11-7 expectations.
Model portfolios span traditional statistical approaches and advanced machine learning, and include key banking risk domains such as credit risk, market risk, and liquidity/treasury models. SR 11-7 emphasizes robust model development, implementation and use, effective independent validation, and strong governance, policies, and controls, all of which are central to this role.
As a Manager, you will bring proven experience leading end-to-end model life cycles, including hands‑on development and independent validation of individual models, plus the operating model, controls, and tooling required to run MRM at scale.
What You Will Do:- Lead and deliver end-to-end quantitative engagements across model development, model validation, model risk governance, and model operations for large financial institutions.
- Serve as a trusted advisor to Model Risk Management leadership, model owners, and senior management on SR 11-7 aligned frameworks, including model lifecycle standards, tiering, and control expectations.
- Drive hands‑on model development for priority use cases (as needed), including problem framing, methodology selection, data strategy, feature engineering, estimation, and implementation in production‑ready code.
- Develop, validate and govern models across multiple domains, such as:
- Credit risk (PD/LGD/EAD, CECL/ACL, stress testing, underwriting and portfolio models)
- Liquidity and treasury models (cash flow forecasting, liquidity risk metrics and reporting)
- Machine learning, GenAI and advanced analytics models
- Build and enhance model governance artifacts: model inventories, model documentation standards, validation policies and procedures, approval workflows, and issue management routines aligned to regulatory expectations.
- Design and implement model operations capabilities (Model Ops/MLOps) that enable repeatability and auditability: version control, testing, reproducibility, lineage, monitoring, and evidence capture across the lifecycle.
- Lead work streams and manage delivery teams, including planning, resourcing, quality review, and executive‑ready communication of complex quantitative topics.
- Coach, mentor, and review the work of consultants and senior consultants.
- Serve as a confident, credible, and compelling client‑facing leader.
- Lead pursuits, proposals, and client presentations.
- Build and deepen long‑term client relationships rooted in trust and delivery excellence.
- Bachelor’s degree in a quantitative discipline such as finance, economics, statistics, mathematics, engineering, or computer science.
- 8+ years of experience in quantitative modeling, model development, model validation, and/or model risk management within financial services, including large financial institutions.
- Demonstrated, hands‑on experience developing models and independently validating individual models, including documentation and defensible reporting.
- Thorough working knowledge of SR 11-7 requirements and expectations related to model development, model validation, and governance, policies, and controls.
- Experience validating and/or governing models in multiple domains, such as machine learning, credit risk, market risk, and liquidity/treasury.
- Strong project management and stakeholder management capabilities
- Advanced degree (e.g., Masters or PhD) in a quantitative…
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