Deep Learning Quant Researcher — Live Trading Impact

J K Barnes; New York (NY); On-site; USD 180,000 - 300,000; Full time

Job summary

JK Barnes is partnering with a leading quantitative trading firm to find a Deep Learning Researcher to develop predictive signals and trading strategies across mid- to high-frequency markets. You will explore large datasets, design neural architectures, and translate research into live trading.

The role offers ownership of ideas from hypothesis to implementation, back-testing experiments, and potential compensation linked to PnL.

Qualifications

• PhD in Mathematics, Statistics, Computer Science or a related quantitative field.
• Expertise in deep learning and neural architectures, especially generative models or reinforcement learning.
• Strong foundations in applied mathematics, statistics and optimisation.
• Experience with time-series analysis and testing ideas on large datasets.

Responsibilities

• Research and develop deep learning models for predictive signals and trading strategies.
• Identify signals from market data and alternative datasets.
• Design experiments, back-test strategies and contribute to simulation tools.
• See impact of research in live trading with performance-linked compensation.

Skills

Python

C++

Time-series analysis

Reinforcement learning

Generative models

Education

PhD in Mathematics/Statistics/CS

Tools

PyTorch

TensorFlow

Backtesting tools

Job description

JK Barnes is partnering with a leading quantitative trading firm to find a Deep Learning Researcher to develop predictive signals and trading strategies across mid- to high-frequency markets. You will explore large datasets, design neural architectures, and translate research into live trading.

The role offers ownership of ideas from hypothesis to implementation, back-testing experiments, and potential compensation linked to PnL.