Quantitative Researcher Intern

• Support the design and backtest of systematic portfolio allocation and rebalancing strategies using Python and our in house framework.

• Research and prototype machine learning techniques (e.g. clustering, regression, reinforcement learning) to tailor asset mixes for individual clients.

• Source, clean and engineer features from market data and alternative data streams (news sentiment, ESG scores, etc.).

• Build automated monitoring tools and dashboards to track live strategy performance, detect drift, and trigger model retraining.

Qualifications:

• Pursuing a degree in Finance, Mathematics, Statistics, Computer Science, or a related quantitative field.

• Proficient in Python (pandas, NumPy) and familiar with ML libraries such as scikit-learn, TensorFlow or PyTorch. 

• Solid grasp of portfolio theory (mean-variance optimization, risk-parity) and quantitative finance concepts.

• Experience or strong interest in robo-advisory platforms, automated trading systems, or digital wealth management.

• Excellent analytical problem‑solving skills and attention to detail in model development and evaluation.

• Effective communicator who thrives in cross‑functional teams and can present complex findings clearly.