We are seeking a Quantitative Research Intern to serve as the bridge between strategy conception and empirical validation. Your primary focus will be the rigorous backtesting of systematic trading strategies developed by our senior team. This role is ideal for a candidate who is highly disciplined, data-driven, and thrives on the technical challenge of translating abstract ideas into high-fidelity simulations.

• Strategy Translation: Convert strategy descriptions and logic into robust Python backtests.
• Sensitivity Analysis: Stress-test strategy parameters, including transaction costs, slippage, and latency to ensure real-world viability.
• Performance Reporting: Analyze and present comprehensive performance metrics (Sharpe, Max Drawdown, Profit Factor, etc.) to assist in capital allocation decisions.

• Python Proficiency: Strong working knowledge of Python, specifically the data science stack (Pandas, NumPy, Matplotlib/Plotly).
• Statistical Foundation: Understanding of statistical techniques, time-series analysis, and probability.
• Attention to Detail: A "skeptic’s mindset" regarding backtest results; ability to spot unrealistic returns or implementation flaws.
• Academic Background: Currently pursuing or recently completed a degree in a quantitative field (Math, Physics, CS, Finance, or Engineering).

• Insight: Direct exposure to the mechanics of professional trading strategies.
• Mentorship: Work closely with a senior team in an active trading environment.
• Real-World Constraints: Learn how to account for market impact, liquidity, and execution costs that academic models often ignore.