Quantitative Researcher - Ownership on signals with PnL contribution payouts

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

This position is also open in Chicago, and London.

For exceptional researchers, the most compelling opportunities offer more than an interesting modelling problem. They offer the chance to own ideas, see them deployed and share in the value they create.

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.

Deep learning sits at the heart of this opportunity. The successful researcher will explore complex market and alternative datasets, develop and test novel neural architectures, and work alongside traders and engineers to translate research into live trading.

The opportunity

• Research and develop deep learning models, including generative models or reinforcement learning.


• Identify predictive signals through the analysis of market data, microstructure and alternative datasets.


• Take ownership of research ideas and signals from initial hypothesis through to implementation.


• Design experiments, back-test strategies and contribute to simulation tools.


• See the impact of your research in live trading, with compensation linked to your contribution to PnL.

What we’re looking for

• A PhD in Mathematics, Statistics, Computer Science or a related quantitative discipline.


• Expertise in deep learning and neural architectures, particularly generative models or reinforcement learning.


• Strong foundations in applied mathematics, statistics and optimisation.


• Experience with time-series analysis, signal processing, statistical modelling or related methods.


• The ability to work with large datasets and test research ideas rigorously.


• Programming skills in Python and C++.

Relevant experience may have been developed through academic research, coursework or industry.

#DeepLearning #MachineLearning #QuantResearch #QuantitativeTrading #AIResearch