Electronic foreign exchange is a cornerstone of State Street's global markets franchise, connecting clients to liquidity, exchanges, and trading venues around the world. As our eFX business continues to expand across market-making, algorithmic execution, and benchmark services, we are investing in the next generation of talent to drive innovation and growth. This is an exciting opportunity to join our eFX Benchmark team, where you will help develop and enhance automated trading strategies that intelligently manage and hedge the market risk associated with client FX benchmark orders.
Why this role is important to usThe team you will be joining is a part of State Street Markets. As a leading provider of trading and lending solutions to the world's institutional investors, we deliver the industry's most innovative platforms, financing and portfolio solutions. Our capabilities are backed by proprietary, high-value research, insights and indicators that power clients' investment decisions, accelerate performance and help investors stay ahead of shifting markets. Across our comprehensive set of solutions - data-driven macro market intelligence that give an information advantage; client-first platforms and tools that redefine trading; financing solutions that streamline liquidity access; and portfolio solutions designed to help achieve peak performance - we deliver a breakthrough edge to drive business success.
Our eFX trading desk processes billions of data points daily, creating challenging quantitative problems across market microstructure, statistical modeling, execution optimization, and real time decision making. We are seeking a talented quantitative researcher with strong technological skills to contribute across multiple aspects of the eFX Benchmark business. This role offers extensive opportunities for professional development, mentoring, and access to advanced resources. If you're passionate about innovation in eFX and looking for a place where your expertise can help drive our continued success, we'd love to hear from you.
Join us if making your mark in the capital markets industry from day one is a challenge you are up for.
Due to the role requirements this job needs to be performed primarily in the office with some flex work opportunities available.
What you will be responsible forAs an eFX Quant Analyst you will:
- Apply expertise in computer science, statistics, optimization, and machine learning to complex, high-frequency, asynchronous market and execution data, and develop automated strategies for benchmark execution, and electronic risk hedging.
- Work across the full research lifecycle, from identifying opportunities and developing hypotheses through data analysis, simulation, implementation, testing, production deployment, while collaborating closely with quantitative researchers, developers, traders, and partners across Technology, Product, Risk, Compliance, and Model Risk.
- Join a dynamic, supportive team where your contributions will directly influence our strategic direction. We value diverse perspectives and innovative approaches, and your ideas will be heard.
More specificallyWhat we value
- Creative and rigorous thinker motivated by challenging problems
- Self-starter with intellectual curiosity and a strong desire to learn
- Ability to formulate research hypotheses, design appropriate tests, and draw well-supported conclusions from complex data.
- Strong knowledge of statistics, probability, optimization, numerical methods, or machine learning
- Experience analyzing large, complex datasets and converting the results into practical insights or solutions.
- Experience developing analytics, visualizations, or user interfaces for data analysis and strategy monitoring.
- Ability to combine quantitative research with practical software development.
- Strong written and verbal communication skills, including the ability to explain complex quantitative concepts clearly.
- Ability to work independently while collaborating effectively across quantitative, trading, technology
- Ability to provide domain expertise on relevant business areas
- Interest in electronic trading, market microstructure, algorithmic execution, and financial markets.
Desired Skills and Experience- Advanced degree in Physics, Mathematics, Statistics, Computer Science, Engineering, Operations Research, Machine Learning, Data Science, or another quantitative discipline. PhD preferred.
- Demonstrated ability to conduct independent quantitative research and translate findings into implementable solutions.
- Experience working with large datasets and applying statistical or quantitative methods to real-world problems.
- Experience with a data analysis and modeling language, such as Python or MATLAB.
- Experience developing in a production programming language, such as Java or C++.
- Experience backtesting and implementing signals using high-frequency, asynchronous tick data.
- Understanding of software development practices, including testing, documentation, code review, and version control using Git.
- Experience using AI-assisted coding tools throughout the development lifecycle.
- Two to three years of eFX trading experience.
Salary Range: $175,000 - $175,000 Annual
The range quoted above applies to the role in the primary location specified. If the candidate would ultimately work outside of the primary location above, the applicable range could differ.
Employees are eligible to participate in State Street's comprehensive benefits program, which includes: our retirement savings plan (401K) with company match; insurance coverage including basic life, medical, dental, vision, long-term disability, and other optional additional coverages; paid-time off including vacation, sick leave, short term disability, and family care responsibilities; access to our Employee Assistance Program; incentive compensation including eligibility for annual performance-based awards (excluding certain sales roles subject to sales incentive plans); and, eligibility for certain tax advantaged savings plans.
For a full overview, visit https://hrportal.ehr.com/statestreet/Home.
