Global Stock Selection Research VP/ED - Technical Signals

About the role

This role owns the design and implementation of technical signal generation within the Global Stock Selection framework at AQR. The hire will perform statistical and economic research using alternative and traditional financial data to develop new alpha signals that stand up to rigorous testing. They will build alpha-generating signals from scratch, including cleaning and processing large-scale raw data with effective programming tools and feature engineering. The position requires engagement with most recent academic and practitioner literature to ensure signals are grounded in sound theory and practice. Collaboration is central, as the role works closely with portfolio managers, risk managers, and traders to improve current investment strategies. Researchers manage all aspects of the research process including data ingestion, methodology selection, implementation, prototyping, and performance evaluation. The role is expected to be hands-on and self-sufficient in conducting end-to-end research projects from idea generation to empirical validation. Success is measured by the ability to deliver robust, economically meaningful signals that enhance portfolio outcomes over time.

Key facts

What you'll do

Perform statistical and economic research using alternative and traditional financial data to develop new alpha signals within the Global Stock Selection group.

Build alpha-generating signals from scratch, cleaning and processing large-scale raw data using effective programming tools and feature engineering based on economic and mathematical intuitions.

Construct, train, and fine-tune machine learning architectures for cross-sectional or time-series prediction with an emphasis on rigorous evaluation.

Engage with the most recent academic and practitioner literature to ensure signals reflect current thinking and empirical realities.

Conduct research on trading cost models, risk models, optimization, and portfolio construction as they relate to signal implementation.

Add features to proprietary research systems to enable rapid prototyping and deployment of new research ideas.

Collaborate with researchers, portfolio managers, risk managers, and traders to refine signals and improve existing investment strategies.

Systematically evaluate the effectiveness of signals through structured testing and performance attribution.

Maintain high standards of intellectual honesty and transparency in all research activities.

Contribute to a culture of academic excellence by challenging assumptions and questioning results within the team.

Requirements

B.S. degree from a top institution in computer science, engineering, mathematics, statistics, operations research, physics or another quantitative discipline. Advanced degrees preferred.

7+ years' experience working in a data driven research environment with an alpha focus.

Experience in quantitative research at a top asset manager or hedge fund preferred.

Proficiency in Python required for data manipulation and implementation tasks.

Ability to manipulate large financial data sets for empirical research and handle complex systems efficiently.

Strong quantitative skills with demonstrated understanding of mathematics, probability, and data science.

Experience with technical or market data based alpha sources at medium and long forecast horizons preferred.

Ability to work independently as well as part of a team in a highly collaborative research environment.

Demonstrated ability to express and articulate ideas and thought processes in both verbal and written form with clarity and precision.

Nice to have

Only items indicated in SOURCE are included; no additional preferences are added.

Practical notes

The role is full-time and based in Greenwich, CT. Compensation is quoted in good faith as a salary range between $255,000 and $275,000, subject to variation based on candidate qualifications and business needs. The position requires collaboration across research, portfolio management, risk, and trading teams. No specific travel, visa, or deadline information is provided in the source material.