Systematic Portfolio Trading and Credit Quant Position

Bank of America•New York, NY

2d•$200,000 - $225,000•Onsite

About The Position

This job is responsible for conducting quantitative analytics and modeling projects for specific business units or risk types. Key responsibilities include developing new models, analytic processes, or systems approaches, creating technical documentation for related activities, and working with Technology staff in the design of systems to run models developed. Job expectations include having a broad knowledge of financial markets and products. This job is responsible for conducting quantitative analytics, systematic trading research, and modeling projects for credit trading businesses, with approximately 80% focus on systematic portfolio trading and 20% focus on credit pricing analytics. Key responsibilities include developing new models, analytic processes, systematic trading approaches and desk-facing tools; creating technical documentation; and partnering with Technology to deploy developed systematic models and scalable analytics into production. The role requires broad knowledge of financial markets and credit products, strong quantitative judgment, and the ability to translate trading needs into robust analytics delivered on tight timelines. The position sits in the Quantitative Strategies and Data Group (QSDG) supporting credit businesses including IG/HY bonds, portfolio trading, FICC ETFs, credit derivatives and related credit products. The primary emphasis is on systematic portfolio trading: systematic pricing and risk management for bonds and portfolio trades, ETF create/redeem proposal generation, execution support, hedging analytics, and production-quality trading tools. A secondary but important component supports the credit pricing analytics stack, including pricing and risk methodology, production implementation, testing, and integration into firm platforms.

Requirements

Strong Python skills for quantitative research, data analysis, workflow automation, and production-quality desk tooling.
Solid understanding of credit bonds, portfolio trading mechanics, ETFs, liquidity, risk, hedging, and transaction-cost-aware decisioning.
Experience with systematic pricing, scenario analysis, and large-scale market or trading datasets.
Working knowledge of credit pricing and risk concepts used by trading desks, including model implementation, testing, and production support.
C++ experience is preferred for performance-critical analytics and integration with core pricing libraries, though the role is primarily Python and systematic trading focused.
Strong software engineering discipline in structured SDLC environments, including version control, testing, CI/release processes, code reviews, and production support practices.
Excellent quantitative problem-solving skills, market intuition, communication ability, and judgment in prioritizing desk-facing deliverables.

Responsibilities

Develop systematic pricing and risk management methods and trader tools that generate fast, explainable indicative ranges for bonds and portfolio trades, with clear diagnostics and practical desk controls.
Create ETF create/redeem proposals, including basket construction, optimization, hedging recommendations, monitoring, and post-trade diagnostics for live trading workflows.
Research, prototype, and productionize systematic portfolio trading strategies and algorithms, including automation, hedge selection, risk management, execution scheduling, and trading controls.
Analyze large datasets across quotes, trades, holdings, constituents, liquidity, market data, and risk to improve tools, strategies, pricing accuracy, and trading outcomes.
Support core credit pricing and risk analytics by contributing to model design, implementation, testing, release, production support, and integration into front-office platforms.
Use Python for research, prototyping, workflow automation, data analysis, and production desk tooling; contribute to C++ components when needed for performance-critical credit pricing analytics.
Partner directly with traders, quantitative developers, and Technology to deliver robust, scalable analytics into production systems with appropriate monitoring, testing, and operational discipline.
Produce clear technical documentation, testing evidence, and governance materials for models, analytics, and desk-facing tools.
Communicate clearly with stakeholders on methodology, limitations, delivery status, priorities, and risk items; help manage trade-offs between speed, robustness, and desk impact.