About the job
Join Trexquant, a pioneering systematic hedge fund leveraging advanced statistical algorithms to trade across global markets, including equities and futures. Our mission is to harness extensive data sets to develop innovative features and apply machine learning techniques to uncover trading signals, amalgamating them into market-neutral portfolios. We are on the lookout for exceptional talent from diverse fields such as data science, physics, engineering, economics, and programming to create the next wave of machine learning strategies that accurately forecast the movements of liquid financial assets.
As a Quantitative Researcher, you will be integrated into one of our specialized teams:
Alpha Researcher: In this role, you will focus on generating market-neutral signals, analyzing substantial data sets, and collaborating with our Data and Strategy Research team to construct a varied array of predictive models.
Strategy Researcher (Machine Learning Track): Here, you will be tasked with creating systematic strategies utilizing a range of machine learning and statistical methodologies, with data derived from real market trades.
Upon applying for a Quantitative Researcher position, we will evaluate your core competencies to ensure a fitting match with our research teams that align with your skills and interests.
Key Responsibilities:
- Develop medium-frequency, market-neutral signals to predict future stock returns.
- Design, implement, and optimize machine learning models to anticipate liquid asset movements using a comprehensive set of financial data.
- Analyze data sets to inform future alpha (strategy) development.
- Explore and apply cutting-edge academic research in quantitative finance.
- Collaborate with seasoned quantitative researchers to conduct experiments and test hypotheses through simulations.
Qualifications:
- BS/MS/PhD in a STEM field.
- Strong enthusiasm for machine learning applications.
- Proficient in programming languages, particularly Python.
- Excellent problem-solving capabilities.
- Able to work independently as well as collaboratively within a team.
- Familiarity with financial accounting is an asset.
- A background in quantitative finance is advantageous but not required.
Benefits:
- Attractive compensation package with performance-based bonuses tied to the algorithms you develop.
- Collaborative and supportive work environment, involving you in the decision-making process regarding research directions, with opportunities to lead new initiatives.

