Trexquant

Quantitative trading via ML models

Head of Systematic ETF Strategy Team

Quantitative Trading / Algorithm DevelopmentConfirmed live in the last 24 hours

Full-timeStamford, CT, USAIn-person

About the Job

Requirements

At least 5 years of experience researching and trading quantitative ETF-based strategies.
A Bachelor's, Master's, or Ph.D. in Mathematics, Statistics, Computer Science, or a related STEM field.
Proven leadership experience managing quantitative research teams.
Proficiency in Python.
Familiarity with large-scale data analysis and backtesting frameworks is a plus.

Responsibilities

Lead and mentor a team of researchers to expand ETF capabilities by identifying new data sources, signals, and strategies.
Oversee the design, backtesting, and implementation of systematic ETF trading strategies.
Collaborate with the development team to enhance the performance, robustness, and scalability of ETF simulation and trading infrastructure.
Partner with execution and financing teams to optimize trade execution and capital efficiency.
Work with the risk team to establish monitoring frameworks, controls, and capital allocation processes specific to ETF exposures.
Present ETF research initiatives and progress to senior management, ensuring alignment with firm-wide trading and investment strategies.

Head of Systematic ETF Strategy Team (USA)

Trexquant Investment · Stamford, United States · Full-time · Posted 2025-08-21

Workplace: on_site

Department: Quantitative Research and Machine Learning

Description

Trexquant is seeking an experienced quantitative researcher to lead our Systematic ETF Strategy Team. In this role, you will manage a team of researchers focused on designing, implementing, and trading systematic ETF-based strategies within Trexquant’s core quantitative framework. Your leadership will play a critical role in scaling ETF strategies into a meaningful asset class for the firm.

Responsibilities

Lead and mentor a team of researchers to expand ETF capabilities by identifying new data sources, signals, and strategies.
Oversee the design, backtesting, and implementation of systematic ETF trading strategies.
Collaborate with the development team to enhance the performance, robustness, and scalability of ETF simulation and trading infrastructure.
Partner with execution and financing teams to optimize trade execution and capital efficiency.
Work with the risk team to establish monitoring frameworks, controls, and capital allocation processes specific to ETF exposures.
Present ETF research initiatives and progress to senior management, ensuring alignment with firm-wide trading and investment strategies.

Requirements

5+ years of experience researching and trading quantitative ETF-based strategies.
Bachelor’s, Master’s, or Ph.D. in Mathematics, Statistics, Computer Science, or a related STEM field.
Proven leadership experience managing quantitative research teams.
Strong quantitative, analytical, and problem-solving skills.
Proficiency in Python; familiarity with large-scale data analysis and backtesting frameworks a plus.

Benefits

Competitive salary with performance-based bonus.
Collaborative, casual, and friendly work environment.
PPO health, dental, and vision insurance fully covered for you and your dependents.
Pre-tax commuter benefits.
Weekly company-provided meals.

The base salary is $200,000. 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

Apply

Apply at Trexquant Investment

About the Company

Trexquant

Quantitative trading via ML models

Trexquant is a quantitative finance firm that builds multi-asset portfolios using machine learning. It develops investment and research tools that ingest a wide range of data variables to create complex trading models and strategies, which in turn generate trading signals. These signals guide decisions across global markets. The product works by applying advanced machine learning to large datasets to create predictive models and rule-based strategies, which drive portfolio construction and trading actions through the firm’s platform. Trexquant differentiates itself by emphasizing the breadth of data it uses and the depth of machine-learning driven models to craft multi-asset portfolios, aiming to produce signals that outperform global market conditions. The company’s goal is to generate investment performance that beats benchmarks by using data-driven signals and diversified, multi-asset strategies.

Company size: 51-200
Company stage: Not listed
Total funding: Not listed
Headquarters: Stamford, CT
Founded: 2012

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