Quantitative Researcher - Trading & Execution

The Role 

We are seeking an experienced researcher to join the Execution Services team, focusing on systematic execution and trading research globally. This candidate will be part of a team committed to enhancing the firm’s execution processes through quantitative methods and advanced technologies. The role involves close collaboration with portfolio managers, traders, technologists, operations specialists, and brokers to conduct research, develop electronic trading algorithms and solutions, and build analytics and tools.

What you’ll do

The Quantitative Execution Researcher will integrate execution research into trading decisions, focusing on reducing trading costs through rigorous measurement, research, and experimentation. The successful candidate will establish a framework to capture and normalize all datasets necessary for measuring and monitoring systematic execution. Responsibilities include estimating statistical significance in execution performance across algorithms and parameters, maintaining strategy routing tables to probabilistically direct flow across brokers and algorithms, developing and calibrating market impact models for real-time systems and simulations, evaluating academic research relevant to trading costs, market microstructure, and micro-pricing, and providing expertise to the development team on optimal order scheduling, routing, slicing, and venue selection.

What you’ll bring
What you need:

• PhD or master's degree, ideally in mathematics, statistics, computer science, financial engineering, or a related field.


• Minimum of 3 years of experience in execution research and algorithmic trading.


• Self-starter with an entrepreneurial spirit and a passion for quantitative finance.


• Excellent analytical skills with a strong foundation in modeling, statistics, probability, and optimization.


• Strong knowledge of equities trading, market microstructure/micro-pricing, and hedge fund operations.


• Solid programming skills, including but not limited to Linux, KDB/Q, and Python.


• Ability to manage multiple tasks in a fast-paced environment with strong attention to detail.


• Excellent communication skills and the ability to collaborate effectively with others.

We’d love if you had:

• Experience with optimization, machine learning, and data visualization.


• Ability to work independently and identify opportunities to create and improve systems without direction.


• Prior experience in trading signals and/or alpha research is a plus

Who we are  

Schonfeld is a global multi-manager hedge fund that strives to deliver industry-leading risk-adjusted returns for our investors. We leverage both internal and external portfolio manager teams around the world, seeking to capitalize on inefficiencies and opportunities within the markets. We draw from decades of experience and a significant investment in proprietary technology, infrastructure and risk analytics to invest across four main strategies: Quant, Tactical, Fundamental Equity and Discretionary Macro & Fixed Income.

Our Culture

At Schonfeld, we’ll invest in you. Attracting and retaining top talent is at the heart of what we do, because we believe that exceptional outcomes begin with exceptional people. We foster a culture where talent is empowered to continually learn, innovate and pursue ambitious goals. We are teamwork-oriented, collaborative and encourage ideas—at all levels—to be shared. As an organization committed to investing in our people, we provide learning and educational offerings and opportunities to make an impact. We encourage community through internal networks, external partnerships and service initiatives that promote inclusion and purpose beyond the firm’s walls.

The base pay for this role is expected to be between $175,000 and $250,000. The expected base pay range is based on information at the time this post was generated. This role may also be eligible for other forms of compensation such as a performance bonus and a competitive benefits package. Actual compensation for the successful candidate will be determined based on a variety of factors such as skills, qualifications, and experience.

#LI-DK1