Quantitative Researcher (New York)

Role Overview

We are seeking an experienced Quantitative Researcher to join Systematic Trading at Jain Global.

The role is focused on systematic global equities research, with holding horizons ranging from overnight to multi-day and longer-term.

We are looking for researchers with a demonstrated track record of developing successful quantitative signals and investment strategies. The role offers the opportunity to contribute across the broader investment process, including data exploration, signal research, modeling, portfolio construction, strategy implementation, and productionization.

You will own research from idea generation through strategy development, validation, implementation, and ongoing evaluation. You will work closely with other quantitative researchers, developers, and technologists to identify new sources of alpha, improve existing strategies, and build robust and scalable systematic investment processes.

The ideal candidate combines strong quantitative and programming skills with sound research judgment, intellectual curiosity, and a demonstrated ability to translate complex datasets and market observations into robust, actionable investment insights.

Responsibilities

• Conduct independent quantitative research to identify, develop, and evaluate systematic global equity strategies and alpha signals.
• Generate research hypotheses and apply rigorous empirical methods to determine whether observed relationships are robust, economically meaningful, and likely to generalize out of sample.
• Analyze large and complex datasets—including market, fundamental, and alternative data—to uncover predictive relationships and investment opportunities.
• Develop, test, and validate statistical and machine-learning models for return forecasting, signal construction, portfolio construction, and trading.
• Own research projects through the full lifecycle, from hypothesis generation, data exploration, and feature engineering through backtesting, implementation, and ongoing performance evaluation.
• Identify and evaluate new datasets and develop differentiated features and signals from market, fundamental, and alternative data.
• Collaborate closely with quantitative researchers, developers, and technologists to translate successful research into scalable production strategies.
• Evaluate and improve existing signals, models, and research frameworks, with particular attention to robustness, overfitting, transaction costs, market frictions, and out-of-sample performance.
• Leverage modern research infrastructure—including AI-assisted tools, agentic workflows, and scalable CPU/GPU compute—where useful to accelerate data analysis, experimentation, model development, and implementation.
• Communicate research findings, assumptions, risks, and investment implications clearly, and contribute to the broader research and investment process.

Qualifications & Experience

• Degree in Mathematics, Computer Science, Statistics, Physics, Engineering, Machine Learning, Computational Finance, or another highly quantitative field.
• Typically 3+ years of relevant quantitative research experience in systematic investing, quantitative trading, or a closely related field.
• Demonstrated experience developing successful systematic equity signals or strategies with holding horizons ranging from overnight to multi-day and longer-term.
• Experience conducting quantitative research in global equities.
• Demonstrated ability to independently generate, develop, evaluate, and iterate on quantitative investment ideas.
• Strong programming skills in Python and familiarity with modern quantitative research and data-analysis tools.
• Strong understanding of statistical modeling, cross-sectional and time-series analysis, machine learning, and linear and non-linear modeling techniques.
• Experience working with large, noisy, and complex datasets and extracting robust, economically meaningful signals.
• Strong understanding of research methodology, including backtesting, model validation, overfitting, transaction costs, and out-of-sample evaluation.
• Ability to combine rigorous quantitative analysis with economic intuition and practical investment judgment.
• Strong communication skills and the ability to collaborate effectively with researchers and technologists in a fast-paced research environment.