Quantitative / Machine Learning Intern

About this role

Investment Team | Singapore 

The Quantitative / Machine Learning Intern will research and develop deep learning models for financial time series and cross-asset signals in a fast-paced, multi-strategy hedge fund environment. The role involves feature engineering across market, fundamental, and macro data, rigorous backtesting, and analysis of model robustness under different regimes. Interns work end-to-end—from research to production-quality code—within a small pod structure, receiving direct mentorship from portfolio manager and senior researchers.

Key Responsibilities

• Research and implement deep learning models for financial time series and cross-asset signals

• Develop shared representations across asset classes and macro regimes

• Design and evaluate features combining market, fundamental, macro, and derived data

• Perform rigorous back testing, stress testing, and regime analysis

• Study model robustness, failure modes, and non-stationarity

• Contribute production-quality research code integrated into live systems

• Present research findings to the PM and investment team

  
  

Research & Engineering Environment

• Small pod structure with no separation between research and engineering

• High ownership across the full research lifecycle

• Rapid iteration and short feedback loops

• Direct mentorship from the PM and senior researchers

• Infrastructure built on cloud-based (AWS) research and trading systems

  
  

Key Requirements

• MS or PhD candidate in Data Science, Machine Learning, Quantitative Finance, CS,

• Statistics, Mathematics, or related fields

• Strong foundations in machine learning, deep learning, and statistical modelling

• Hands-on experience with deep learning frameworks (e.g., PyTorch)

• Strong Python skills and experience working with large datasets

• Familiarity with cloud-based environments (AWS) preferred

• Interest in global macro dynamics and cross-asset modelling

• Comfortable working with ambiguous, open-ended research problem

  
  

To Apply:
Please send your resume and a brief cover note outlining your interest and relevant experience to HR@arrowpointfund.com.