Portfolio Optimization Associate

Job Description

We are looking for a Portfolio Optimization Associate to support the research, optimization, and allocation of capital across a diverse set of quantitative trading strategies. This role sits at the intersection of quant research, trading, and risk — and plays a key part in improving the overall efficiency and performance of our strategy portfolio.

You will work closely with senior portfolio managers to ensure capital is allocated effectively, taking into account risk, correlation, and return profiles.

Responsibilities

• Assist in capital allocation decisions across multiple trading strategies, desks, and regions.
• Conduct portfolio optimization using risk-adjusted return metrics, drawdown analysis, and correlation clustering.
• Build, improve, and maintain internal tools to support real-time portfolio analytics and allocation dashboards.
• Collaborate with quant teams to analyze performance drift, strategy capacity, and risk decomposition.
• Provide data-driven insights and recommend adjustments to enhance Sharpe, reduce exposure overlap, or improve capital usage efficiency.
• Contribute to monthly reviews and internal reporting on portfolio structure and performance attribution.

Requirements

• Bachelor’s or Master’s degree in a quantitative field such as Financial Engineering, Mathematics, Statistics, Computer Science, or a related discipline.

• 1–3 years of experience in a trading firm, hedge fund, or asset management role focused on portfolio construction, optimization, or multi-strategy allocation.

• Strong understanding of portfolio theory, risk-adjusted metrics (e.g., Sharpe, Sortino, max drawdown), and factor modeling.

• Experience with Python (Pandas, NumPy, etc.) for data analysis; familiarity with SQL and/or visualization libraries (e.g., Plotly, Dash) is a plus.

• Comfort working with large datasets, strategy-level performance data, and time-series analysis.

• Excellent communication skills and ability to present quantitative findings to technical and non-technical stakeholders.

• Highly detail-oriented with a proactive mindset and strong sense of ownership

• Good to have:

• Knowledge of systematic trading strategies (e.g., stat arb, trend following, market making).

• Familiarity with risk models, capacity analysis, or intraday exposure tracking.