Quantitative Researcher
HWTS Global, New York, NY, United States
Quant Researcher (Machine Learning – Equities) | London / New York | Multi-Strategy Platform
We’re partnering with a top-tier multi-strategy hedge fund looking to hire an Equity Quant Researcher with a strong ML focus. This role sits within a high-performing systematic equities team, focused on building scalable, data-driven alpha using modern machine learning techniques.
The team operates in a fast-paced, performance-led environment with strong infrastructure and access to diverse datasets. You’ll be working closely with experienced PMs and researchers to push forward next-generation equity signals.
What You’ll Be Doing
Design and develop machine learning-driven alpha signals for systematic equity strategies
Work with large, complex datasets to extract predictive features and insights
Take ownership of the full research lifecycle: ideation, testing, validation, and deployment
Collaborate with PMs to translate research into live trading strategies
Continuously refine models with a focus on robustness, scalability, and real-world performance
Contribute to the evolution of the team’s research framework and data infrastructure
Core Requirements
5+ years’ experience in systematic equity research with a track record of deployable alpha generation
Strong intuition for market dynamics, including liquidity, participant behaviour, and changing risk regimes
Advanced Python programming skills, with experience in research and production environments
Deep experience working with core financial datasets (e.g. prices, fundamentals, risk factors)
Proven ability to handle large, imperfect datasets and navigate real-world data challenges
Strong understanding of backtesting frameworks and common pitfalls (e.g. overfitting, look-ahead bias, transaction costs)
Excellent academic background in a quantitative field
Nice to Have
Experience working with high-frequency or granular (tick-level) equity data
Hands‑on application of machine learning techniques (e.g. tree-based models, deep learning, NLP/LLMs) in alpha research
Familiarity with market microstructure in developed equity markets
Exposure to C++ or performance-focused programming
Why This Role
Work within a well-resourced, high-performing multi-strat platform
Access to cutting-edge data and compute infrastructure
Strong emphasis on ML-driven research and innovation
Direct path to impactful alpha generation and deployment
#J-18808-Ljbffr
We’re partnering with a top-tier multi-strategy hedge fund looking to hire an Equity Quant Researcher with a strong ML focus. This role sits within a high-performing systematic equities team, focused on building scalable, data-driven alpha using modern machine learning techniques.
The team operates in a fast-paced, performance-led environment with strong infrastructure and access to diverse datasets. You’ll be working closely with experienced PMs and researchers to push forward next-generation equity signals.
What You’ll Be Doing
Design and develop machine learning-driven alpha signals for systematic equity strategies
Work with large, complex datasets to extract predictive features and insights
Take ownership of the full research lifecycle: ideation, testing, validation, and deployment
Collaborate with PMs to translate research into live trading strategies
Continuously refine models with a focus on robustness, scalability, and real-world performance
Contribute to the evolution of the team’s research framework and data infrastructure
Core Requirements
5+ years’ experience in systematic equity research with a track record of deployable alpha generation
Strong intuition for market dynamics, including liquidity, participant behaviour, and changing risk regimes
Advanced Python programming skills, with experience in research and production environments
Deep experience working with core financial datasets (e.g. prices, fundamentals, risk factors)
Proven ability to handle large, imperfect datasets and navigate real-world data challenges
Strong understanding of backtesting frameworks and common pitfalls (e.g. overfitting, look-ahead bias, transaction costs)
Excellent academic background in a quantitative field
Nice to Have
Experience working with high-frequency or granular (tick-level) equity data
Hands‑on application of machine learning techniques (e.g. tree-based models, deep learning, NLP/LLMs) in alpha research
Familiarity with market microstructure in developed equity markets
Exposure to C++ or performance-focused programming
Why This Role
Work within a well-resourced, high-performing multi-strat platform
Access to cutting-edge data and compute infrastructure
Strong emphasis on ML-driven research and innovation
Direct path to impactful alpha generation and deployment
#J-18808-Ljbffr