Prop Trading Firm Seeks - Deep Learning Researcher - New York

[Recruiter-Jim]

Firm: Prop Trading Firm
Location: New York City
Role: Deep Learning Researcher
1st Year Target Comp Range: $500k- $650k

Our client is a small but highly profitable quantitative trading firm specializing in intraday statistical arbitrage and cutting-edge AI applications. With a team of approximately 40 professionals, they foster a collaborative and intellectually stimulating environment. They are committed to pushing the boundaries of financial modeling through advanced hybrid models developed in-house, at the intersection of deep learning, probability and agent-based AI, and are seeking a talented individual to join their research team in New York.

Responsibilities:

• Design, develop, and backtest AI-driven trading models for equities and futures, with a focus on day trading strategies.
• Research and implement advanced machine learning techniques, including agent-based modeling and LLMs, for financial market prediction.
• Conduct rigorous statistical analysis of market data to identify trading opportunities and evaluate model performance.
• Collaborate with other researchers to refine trading strategies and improve model performance.
• Stay up-to-date with the latest research and advancements in AI, machine learning, and quantitative finance.
• Contribute to the firm's research efforts by writing research reports and presenting findings.

Essential Skills and Qualifications:

• PhD in a quantitative field (e.g., Statistics, Mathematics, Physics, Computer Science) with a strong foundation in probability theory (demonstrated by undergraduate coursework) and a focus on deep learning during doctoral studies, including publications in the field.
• 0-2 years of experience in quantitative research or a related field (including internships), ideally with a focus on time series analysis, forecasting, or related areas.
• Strong theoretical understanding of statistical modeling, and deep learning, with demonstrated experience in building and applying deep learning models (e.g., Transformers, CNNs, RNNs, LSTMs, DSSMs, Hybrid Models) to time series data.
• Demonstrated interest and experience in agent-based AI and Large Language Models (LLMs).
• Excellent programming skills in Python, including experience with deep learning libraries (e.g., PyTorch, TensorFlow) and numerical computation libraries (e.g., NumPy, SciPy). Experience with C++ is highly advantageous.
• Experience optimizing code for performance, resulting in improved efficiency and resource utilization through techniques such as GPU acceleration (CUDA, cuDNN) and parallelization (data and task parallelism).
• Strong analytical and problem-solving skills, with the ability to apply quantitative methods to financial problems.
• Excellent communication and presentation skills, both written and verbal.
• Strong interpersonal skills, including humility, a collaborative approach to teamwork, and effective communication.
• Experience with high-dimensional time series analysis and financial data is a significant plus.

James@njf.com