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. As a Quantitative Researcher 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. While we are open to researchers in any asset class we are currently focusing on roles in equities, futures, commodities, and event driven research. 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 and analyze large datasets to identify actionable alpha signals and develop strategies for systematic trading. Investigate and implement state-of-the-art academic research in the field of quantitative finance. Continuously innovate and improve existing models by integrating new data sources and advanced techniques to boost performance and scalability. Collaborate closely with a team of experienced quantitative researchers to conduct experiments, backtest hypotheses, and refine strategies through rigorous simulations and data analysis. Requirements BS/MS/PhD degree in any stem field 2+ years in a systematic trading environment Passion for machine learning Fluent with programming languages like Python Strong problem-solving skills Ability to work effectively both as an individual and a team player BenefitsCompetitive salary plus bonus based on individual and company performanceCollaborative, Casual, and friendly work environmentPPO Health, dental and vision insurance premiums fully covered for you and your dependentsPre-tax commuter benefitsWeekly company mealsApplications are open for both Stamford and New York City offices, the latter with a planned opening in October 2026. The base salary for this role is $130,000 to $200,000, and will be determined based on the candidate’s educational background and professional experience. Base salary is one component of Trexquant’s total compensation package, which may also include a discretionary, performance-based bonus. This position is classified as overtime-exempt.Trexquant is an Equal Opportunity Employer
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.
As a Quantitative Researcher 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. While we are open to researchers in any asset class we are currently focusing on roles in equities, futures, commodities, and event driven research.
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 and analyze large datasets to identify actionable alpha signals and develop strategies for systematic trading.
- Investigate and implement state-of-the-art academic research in the field of quantitative finance.
- Continuously innovate and improve existing models by integrating new data sources and advanced techniques to boost performance and scalability.
- Collaborate closely with a team of experienced quantitative researchers to conduct experiments, backtest hypotheses, and refine strategies through rigorous simulations and data analysis.
Requirements
- BS/MS/PhD degree in any stem field
- 2+ years in a systematic trading environment
- Passion for machine learning
- Fluent with programming languages like Python
- Strong problem-solving skills
- Ability to work effectively both as an individual and a team player
Benefits
- Competitive salary plus bonus based on individual and company performance
- Collaborative, Casual, and friendly work environment
- PPO Health, dental and vision insurance premiums fully covered for you and your dependents
- Pre-tax commuter benefits
- Weekly company meals
Applications are open for both Stamford and New York City offices, the latter with a planned opening in October 2026.
The base salary for this role is $130,000 to $200,000, and will be determined based on the candidate’s educational background and professional experience. Base salary is one component of Trexquant’s total compensation package, which may also include a discretionary, performance-based bonus. This position is classified as overtime-exempt.
Trexquant is an Equal Opportunity Employer
Trexquant applies quantitative methods to systematically build optimized global market-neutral equity portfolios in liquid markets. Trading signals (Alphas) are developed from thousands of data variables and extensively tested. Strategies dynamically adjust allocations to Alphas depending on recent performance. Thousands of strategies using tens of thousands of signals currently drive our live production, and our talented team of researchers from some of the best schools in the world inject new ideas into our system on an ongoing basis. Capital is managed across thousands of equity positions in the United States, Europe, Japan, Australia, and Canada.
Trexquant applies quantitative methods to systematically build optimized global market-neutral equity portfolios in liquid markets. Trading signals (Alphas) are developed from thousands of data variables and extensively tested. Strategies dynamically adjust allocations to Alphas depending on recent performance. Thousands of strategies using tens of thousands of signals currently drive our live production, and our talented team of researchers from some of the best schools in the world inject new ideas into our system on an ongoing basis. Capital is managed across thousands of equity positions in the United States, Europe, Japan, Australia, and Canada.
