Quantitative Researcher (NLP)

Role/Responsibilities:

We are seeking a quantitative researcher to join an NLP quant team within Point72. We believe the significant advances in NLP methods show promise for finance. We develop and launch end-to-end signals, from data processing to performance testing.

The ideal candidate will have strong machine learning, data science and software engineering skills, some experience with modern NLP, and a curiosity about finance and trading.

• Responsibilities may include:
• Using NLP to construct features from varied datasets
• Formulating research hypotheses to derive alpha
• Building and testing the performance of trading signals based on NLP and financial features
• Launching identified signals into production
  
  

Requirements:

• Bachelor’s, Master’s or PhD degree in computer science or other quantitative discipline
• Proficient in Python and general software engineering principles (github, testing, dev workflow)
• Strong understanding of machine learning and statistics
• Prior research experience preferred
• Experience with deep learning, specifically NLP (PyTorch, HuggingFace, LLMs, etc.) preferred
• Data science stack familiarity preferred
• An interest in financial markets
• Great attitude
• A collaborative mindset
• Commitment to the highest ethical standards
  
  

About Point72:

Point72 is a leading global alternative investment firm led by Steven A. Cohen. Building on more than 30 years of investing experience, Point72 seeks to deliver superior returns for its investors through fundamental and systematic investing strategies across asset classes and geographies. We aim to attract and retain the industry’s brightest talent by cultivating an investor-led culture and committing to our people’s long-term growth.

The annual base salary range for this role is $150,000-$250,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.