Director, Credit Quant

Job Description:

At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities and shareholders every day.

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Bank of America is committed to an in-office culture with specific requirements for office-based attendance and which allows for an appropriate level of flexibility for our teammates and businesses based on role-specific considerations.

At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us!

Job Description:
This job is responsible for conducting quantitative analytics and complex modeling projects for specific business units or risk types. Key responsibilities include leading the development of new models, analytic processes, or system approaches, creating technical documentation for related activities, and working with Technology staff in the design of systems to run models developed. Job expectations may include the ability to influence strategic direction, as well as develop tactical plans.

Job Description: The role is a senior front-office quantitative position within the Credit area of Global Markets. It sits in QSDG and supports credit trading businesses (IG/HY bonds, credit ETFs, credit derivatives, structured products, and EM credit). The role owns key components of the credit analytics stack end-to-end: model design, implementation in production, desk tooling, integration into firm platforms, and ongoing enhancements. Success requires strong market intuition, excellent quantitative judgment, and the ability to translate trading needs into robust, well-engineered analytics delivered on tight timelines.

Responsibilities:

• Own and evolve core credit pricing and risk analytics used by the trading desks, including model design, implementation, testing, release, and production support.
• Lead development of performance-critical analytics libraries in C++, including architecture decisions, code quality standards, and runtime performance improvements.
• Partner with Technology to deliver analytics into production systems, improve stability and throughput, and establish monitoring/operational practices suitable for front-office usage.
• Use Python (highly encouraged) for research, prototyping, data analysis, tooling, and workflow automation around the core C++ stack; operate effectively in a mixed C++/Python development workflow.
• Drive quantitative analysis on large datasets (market data, quotes, trades, risk, reference data) and convert results into desk-facing outputs and measurable improvements.
• Lead stress testing and scenario analytics relevant to the business and controls (scenario design/implementation, result aggregation, reporting, and driver analysis).
• Own governance deliverables for models and analytics: clear technical documentation, robust testing evidence, and effective engagement through validation and stakeholder review.
• Provide senior-level methodological guidance and constructive challenge across development/validation efforts; identify key limitations, controls, and remediation priorities.
• Communicate clearly with senior stakeholders on outcomes, limitations, delivery status, and risk items; set priorities and manage trade-offs with the desk.
• Lead and mentor other developers/quants: code reviews, technical design reviews, standards for maintainability, and coaching on production engineering practices.

• Advanced C++ expertise (required), with a track record of owning large production codebases and delivering high-performance analytics libraries. Expectations include strong object-oriented design, API design, memory/latency awareness, and debugging in complex systems.
• Experience with performance engineering (profiling, optimization, algorithmic efficiency), and comfort working with concurrency and production reliability concerns where relevant to analytics delivery.
• Python is highly encouraged for rapid prototyping, research, and desk tooling; experience integrating Python tooling with a C++ core is strongly preferred.
• Strong software engineering discipline in structured SDLC environments (version control, testing, CI/release processes); familiarity with common front-office toolchains is a plus.
• Strong quantitative problem-solving skills and ability to work with large datasets in a reproducible way.
• Solid knowledge of credit products and markets (bonds and credit derivatives) and familiarity with pricing and risk concepts used by trading desks.