About this role:

Wells Fargo is seeking an eRates Algorithmic Trading Strategist to help drive the build-out of client-focused, data-driven trading strategies for electronic Rates trading. This is a high-growth opportunity to shape how algorithms, data, client behavior, and pricing analytics drive business outcomes in a rapidly evolving Macro trading franchise.

The successful candidate will work closely with modeling quants, systematic and voice trading desks, and E-Trading technology teams to translate strategic ideas into scalable, production-ready solutions. The role centers on pricing research, data analytics, client optimization, price differentiation, client alpha generation, and embedding data as a core lever for commercial growth.

Key Responsibilities:

• Implement a client-centric algorithmic data strategy for eRates, with a focus on execution optimization, price differentiation, and systematic liquidity provisioning.
• Partner with modeling strats/quants to design and implement client-centric pricing and execution strategies, leveraging market microstructure and behavioral analytics.
• Help drive the algo data strategy for eRates, focusing on client optimization, execution quality, and systematic liquidity provisioning.
• Work closely with curve modeling quants and non-eTrading stakeholders to align how data is utilized across pricing, risk, and client engagement workflows.
• Research and prototype client optimization methods, client alpha signals, and differentiated pricing frameworks.
• Collaborate closely with technology partners to translate client optimization ideas into scalable, low-latency solutions—ensuring data-driven strategies are effectively implemented and integrated into the trading infrastructure.
• Maintain and enhance libraries of research, analytics, signals, reports, trading dashboards, simulation tools, and performance diagnostics.
• Produce actionable insights and visualizations for trading, sales, and senior stakeholders.
• Champion the use of data as a strategic asset—integral to business growth, client engagement, and revenue generation.

Required Qualifications:

• 7+ years of Securities Algorithmic Trading experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education

Desired Qualifications:

• Minimum 7 years of experience in quantitative analytics or algorithmic trading, with a strong focus on client execution and optimization.
• Proven ability to design and deploy client-focused execution strategies within low-latency trading environments.
• Proven track record in Rates electronic trading with a strong emphasis on leveraging market microstructure and client data to drive execution quality, price differentiation, and alpha generation.
• Master’s degree or higher in Financial Engineering or a related quantitative discipline (e.g., Mathematics, Physics, Computer Science, Engineering).
• Deep understanding of trading signal generation, client-driven alpha discovery, and backtesting frameworks—including techniques for extracting insights from noisy, high-volume data to improve pricing and execution.
• Experience applying client optimization learning techniques, including behavioral modeling, adaptive pricing, and execution feedback loops.
• Familiarity with PnL attribution frameworks to assess strategy performance and client impact.
• Strong programming skills in Python (Pandas, NumPy, SciPy, Scikit-learn), KDB/Q (PyKx, embedpy), and Java/Groovy for low-latency infrastructure.
• Ability to collaborate across trading, quant, and technology teams to deliver scalable, production-grade solutions.
• Excellent communication skills with a track record of translating complex analytics into actionable business insights.

Pay Range

Reflected is the base pay range offered for this position. Pay may vary depending on factors including but not limited to demonstrated examples of prior performance, skills, experience, or work location. Employees may also be eligible for incentive opportunities.

Posting End Date:

*Job posting may come down early due to volume of applicants.