Fixed Income Assoc. Director - Quant
U.S. BankAbout the role
At U.S. Bank, we’re on a journey to do our best. Helping the customers and businesses we serve to make better and smarter financial decisions and enabling the communities we support to grow and succeed. We believe it takes all of us to bring our shared ambition to life, and each person is unique in their potential. A career with U.S. Bank gives you a wide, ever-growing range of opportunities to discover what makes you thrive at every stage of your career. Try new things, learn new skills and discover what you excel at—all from Day One.
Job Description
Role Overview
We are seeking a Front Office Data Scientist/ Quantitative Developer to support our Credit Trading business, with a focus on electronic trading, client flow, and automated pricing. This role sits directly on the Credit trading desk, working closely with Traders, Sales, and Electronic Trading teams to develop quantitative models and analytics that enhance pricing, execution quality, risk management, and liquidity provision.
The ideal candidate combines a strong financial engineering background, deep understanding of credit products and bond mathematics, and hands-on programming expertise to build models that operate in high‑throughput, real‑time trading environments.
Key Responsibilities
Partner directly with Credit Traders and Sales to support:
Electronic market making
Automated pricing and quote generation
Design, implement, and maintain quantitative models for:
Corporate bond and credit curve pricing
Spread, relative value analytics
Inventory management and hedging strategies
Develop and maintain back‑testing and simulation frameworks for credit trading strategies, pricing models, and execution logic
Analyze large volumes of electronic trading data, including client RFQ flow, hit ratios, quote competitiveness, and liquidity metrics
Optimize models for latency‑sensitive and production trading systems
Translate research prototypes into robust, production‑ready tools embedded in electronic trading and risk platforms
Monitor and recalibrate models based on:
Market regime changes
Liquidity conditions
Client behavior and flow dynamics
Collaborate with technology, quants, and risk partners to ensure scalability, resiliency, and regulatory alignment
Required Qualifications
Master’s or PhD in Financial Engineering, Quantitative Finance, Mathematics, Statistics, Physics, Computer Science, or a related field
5-7 years of relevant experience
Strong understanding of Credit markets and bond mathematics, including:
Corporate bonds, IG and HY markets
Z‑spreads, OAS, duration, convexity, DV01
Discounting, curve construction, and term structures
Hands-on programming experience in Python and/or C++, applied to:
Pricing and analytics libraries
Back‑testing and simulation frameworks
Performance‑sensitive, production trading systems
Demonstrated experience with:
Model calibration, optimization, and validation
Quantitative analysis of market and trade data
Solid foundation in probability, statistics, numerical methods, and time‑series analysis
Preferred / Nice-to-Have Skills
Prior experience supporting a sell‑side Credit trading desk
Strong familiarity with electronic credit trading protocols, such as:
RFQ and streaming markets
Client quote distribution and scorecards
Experience with execution analytics, transaction cost analysis (TCA), or liquidity modeling in credit
Knowledge of pricing engines, curve frameworks, and risk systems used in front‑office environments
Experience with cloud platforms (AWS, Azure, or GCP)
Exposure to MLOps practices, including:
Model versioning and governance
Automated deployment and monitoring
Understanding of market microstructure and liquidity dynamics in less‑liquid asset classes
Front Office Skills & Attributes
Commercial mindset with clear awareness of desk P&L, risk limits, and client expectations
Ability to operate under real‑time market conditions and time pressure
Comfortable engaging directly with Traders and Sales on trading floor priorities
Strong communication skills, able to explain quanti
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