Senior Quantitative Risk Model Developer (contract)

Title: Senior Quantitative Risk Model Developer Location: Charlotte, NC Duration: 12 months Work Engagement: W2 Benefits on offer for this contract position: Health Insurance, Life insurance, 401K and Voluntary Benefits Summary: In this contingent resource assignment, you may: Participate in low to moderately complex initiatives and identify opportunity for process improvements within Business Execution. Review and analyze basic or tactical Business Execution assignments or challenges that require research, evaluation, and selection of alternatives, related to low-to-medium risk deliverables. Present recommendations for resolving low to moderately complex situations and exercise some independent judgment while developing understanding of function, policies, procedures, and compliance requirements. Provide information to client personnel in Business Execution. We are seeking a highly analytical Quantitative Model Developer with strong Python engineering skills and deep familiarity with cross‑margining concepts within prime brokerage and capital markets. This role focuses on enhancing and maintaining counterparty credit risk models—not pricing or market risk models—with an emphasis on mathematical rigor, cross‑product methodology development, and hands-on coding. The ideal candidate has a strong mathematical foundation, the ability to derive formulas, identify methodological gaps, and improve model implementations. You will work closely with junior team members, business partners, model owners, technology stakeholders, and project management groups. Because cross‑margin exposure plays a significant and high-impact role in CIB markets, this position requires a strong sense of urgency and responsiveness to ad hoc model requests. Key Responsibilities Modeling & Quantitative Analysis Develop, enhance, and maintain counterparty credit risk models related to cross‑margin methodologies. Derive analytical formulas, validate assumptions, and identify gaps in existing implementations. Improve or replace outdated models using modern stochastic and capital markets modeling techniques. Support modeling across a range of complex financial products, including: Equity swaps Metals Energy derivatives Convertible bonds Technical Development Lead the build‑out and integration of Python-based quantitative libraries to support model development and validation activities. Produce robust prototype models and partner with technology teams to transition them into production. Utilize generative AI development tools (e.g., Copilot) to increase coding efficiency and automation. Collaborate on database queries using strong SQL expertise. Cross‑Functional Collaboration Communicate clearly with model owners, business partners, technology teams, auditors, and project managers. Help translate business requirements into quant/model specifications and documentation. Provide coaching and technical guidance to junior team members on both modeling and cross‑margin concepts. Operational Readiness Respond quickly to urgent model requests driven by high-impact cross‑margin exposures in the CIB business. Ensure timely delivery of model enhancements, documentation, and validations. Qualifications: Applicants must be authorized to work for ANY employer in the U.S. This position is not eligible for visa sponsorship. Python (expert level) – ability to build, structure, and maintain quant libraries. Experience using AI-assisted coding tools (Copilot or similar) SQL expertise – ability to query and manipulate large datasets. Strong numerical skills and experience with stochastic modeling and capital markets models. Ability to derive mathematical formulas and implement them programmatically. Strong understanding of cross‑margining concepts in prime brokerage or derivatives clearing. Ability to identify and correct model gaps, inconsistencies, or legacy issues. Solid foundation in probability, statistics, and stochastic processes. Preferred Skills: Experience in prime brokerage or margin methodology design. Prior work with counterparty credit exposure models (e.g., PFE, EE, EAD). Familiarity with equities, commodities, energy, and structured derivative products.