Quant ML Researcher: Build Market-Predictive Models

Fintal Partners; New York (NY); On-site; USD 150,000 - 210,000; Full time

Job summary

Fintal Partners in New York is seeking a proven quantitative researcher to apply cutting-edge machine learning and deep learning to hard trading problems within a central ML research group.

You will build and deploy models across asset classes, research new algorithmic ideas and collaborate with traders, researchers and engineers to turn market insights into data-driven features and alpha ideas.

Qualifications

• PhD or Master’s in Engineering, Math, Statistics, Computer Science or a comparable quantitative discipline.
• 2+ years building applied ML models; trading environment experience preferred.
• Demonstrated skill developing and deploying predictive foundational models.
• Strong Python, with ML libraries such as PyTorch or TensorFlow and/or high-performance frameworks like JAX.
• Solid grasp of the theory behind state-of-the-art ML models.
• Strong publication record at ICML, ICLR, NeurIPS or equivalent.
• Genuinely collaborative, with excellent written and verbal communication.

Responsibilities

• Build and deploy machine learning models that lift trading performance across asset classes.
• Research, test and prototype new algorithmic ideas, applying advanced ML to market prediction, signal generation and portfolio optimization.
• Work with quantitative traders, researchers and developers to turn market insight into data-driven features and models.
• Own data acquisition, preprocessing and feature engineering across structured and unstructured sources.

Skills

Python

PyTorch

TensorFlow

JAX

ML theory

Communication

Education

PhD or Master’s in Engineering, Math, Statistics, CS

Tools

Git

Job description

Fintal Partners in New York is seeking a proven quantitative researcher to apply cutting-edge machine learning and deep learning to hard trading problems within a central ML research group.

You will build and deploy models across asset classes, research new algorithmic ideas and collaborate with traders, researchers and engineers to turn market insights into data-driven features and alpha ideas.