Trexquant is a systematic hedge fund where we use thousands of statistical algorithms to trade equity, futures and other markets globally. Starting with many data sets, we develop large sets of features and use various machine learning methods to discover trading signals and effectively combine them into market-neutral portfolios. We are looking for data scientists, physicists, engineers, economists and programmers to develop the next generation of machine learning strategies that can accurately predict the future movements of liquid financial assets.
Our Quantitative Researchers are embedded within the following teams:
Alpha Researcher:
As a member of the Alpha Researcher team, you will be involved in developing market-neutral signals, parsing and analyzing large data sets and collaborating with the Data and Strategy Research team to build a diverse set of predictive models.
Data Scientist:
As a member of the Data Science team, you will be involved in parsing and analyzing large data sets, working on discovering and obtaining new sources of data and collaborating with the Alpha and Strategy team to build predictive machine learning models.
Strategy Researcher:
As a member of the Strategy team, you will be developing systematic strategies based on a variety of machine learning and statistical methods. The data you train and validate comes from actual market trading
When you apply for a Quantitative Researcher role at Trexquant, we will first assess you on the core skills required for the Quantitative Researcher. During the interview process, we will be able to get to know you better, learn about your strengths and match you to the best research team that closely aligns with your skills and preference.
Your Responsibilities:
- Design, implement, and optimize various machine learning models aimed at predicting liquid assets using a wide set of financial data and a vast library of trading signals
- Parse data sets to be used for future alpha(strategy) development
- Investigate and implement state-of-the-art academic research in the field of quantitative finance
- Collaborate with experienced and resourceful quantitative researchers to carry out experiments and test hypothesis using simulations
Requirements
- Graduate degree (Bachelors, Masters, or PhD) in any STEM discipline from a prestigious institution
- Passion for machine learning and quantitative finance
- Strong problem-solving skills
- Ability to work effectively both as an individual and a team player
- Proficient in programming languages such as Python
- Previous experience in alpha research and the development of trading signals and strategies
- The role is also open for candidates graduating from Tier 1 campuses in 2027
Benefits
- Competitive compensation with bonus tied to the performance of algorithms you develop
- Work in a collaborative and friendly environment, participate in decision-making process for research direction, and have opportunity to lead on new ideas
- Pre-tax commuter benefits
- Weekly company meals
Trexquant is an Equal Opportunity Employer
Skills Required
- Graduate degree in any STEM discipline
- Passion for machine learning and quantitative finance
- Strong problem-solving skills
- Ability to work effectively both individually and as a team player
- Proficient in programming languages such as Python
- Previous experience in alpha research and development of trading signals
- Experience between 1 years to 10 years
What We Do
Being a quantitative finance firm that uses Machine Learning (ML) to create multi-asset portfolios and seek profit from the market, Trexquant has continuously improved its investment and research platform since starting operations, leveraging new and emerging technologies. Trexquant uses rigorous quantitative methods to create multi-asset portfolios in global markets. To do this, Trexquant develops trading signals using its vast and continuously growing collection of data variables used as inputs for more complex trading models called Strategies. The result is an ever-growing and adapting engine built from thousands of intricate models and tens of thousands of signals, tailor-made with the goal to outperform the market during any condition. Capital is managed across 5,500+ cash equity positions across the United States, Europe, Japan, Australia, and Canada.
