Quantitative Researcher - Experienced Hires (USA)

Trexquant Investment LP; New York (NY); On-site; USD 130,000 - 200,000; Full time

Benefits offered by this job

Competitive salary plus bonus

Full health, dental and vision insurance premiums

Pre-tax commuter benefits

Weekly company meals

Job summary

A systematic hedge fund in New York is seeking an experienced Quantitative Researcher to develop machine learning strategies aimed at predicting liquid assets. The position involves analyzing large data sets, working closely with a team, and implementing advanced quantitative finance techniques. Successful candidates will possess a degree in a STEM field, strong programming skills in Python, and a passion for machine learning. This role offers a competitive salary and a collaborative work environment.

Qualifications

• 2+ years in a systematic trading environment.
• Fluent with programming languages like Python.
• Passion for machine learning.

Responsibilities

• Design and optimize machine learning models for liquid asset prediction.
• Analyze large datasets to identify actionable trading signals.
• Implement state-of-the-art quantitative finance research.
• Collaborate with researchers for hypothesis testing and strategy refinement.

Skills

Machine learning

Programming (Python)

Strong problem-solving skills

Data analysis

Team collaboration

Education

BS/MS/PhD in any STEM field

Job description

Quantitative Researcher - Experienced Hires (USA)

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 now open for our NYC office that will commence operations in September 2026.

Base salary range: $130,000 to $200,000, depending on candidates’ educational and professional background. Base salary is only one part of Trexquant's total compensation, which may include a discretionary and performance‑based bonus.

Trexquant is an Equal Opportunity Employer.