Description

An investment management firm established in 2013 is seeking to hire a new member for its volatility arbitrage team, which focuses on equity-volatility-related strategies. The firm specializes in delivering research-driven, risk-aware, and absolute-return investment approaches, operating within a rapidly evolving economic and market environment.

Key Responsibilities

• Conduct research and implement relative-value volatility strategies, including spread, term-structure, and cross-asset volatility relationships.
• Develop, test, and improve quantitative models used for pricing, risk assessment, and signal generation.
• Evaluate historical and intraday market data to detect market inefficiencies and arbitrage opportunities.
• Design and manage scalable data pipelines-primarily using Snowflake and Python-to support research workflows, feature engineering, and model development.
• Build Python-based tools for backtesting, trade simulation, and performance analysis.
• Work closely with team members to convert research insights into executable trading strategies.
• Communicate research findings, model outcomes, and implementation details clearly to senior team members.

Qualifications

• 2-5 years of experience as a quantitative researcher, strategist, or quantitative developer within a hedge fund, bank, or trading environment.
• Strong proficiency in Python for modeling, research, and data engineering tasks.
• Practical experience with Snowflake or comparable cloud data-warehouse platforms.
• Solid quantitative foundation (e.g., statistics, econometrics, applied mathematics, or financial engineering).
• Ability to operate effectively in a fast-moving, collaborative, and entrepreneurial setting.

Preferred Skills

• Background in systematic strategies such as relative value, dispersion, correlation, or quantitative macro.
• Familiarity with portfolio construction, PnL attribution, and risk-model development.
• An advanced quantitative degree (MS/PhD) is advantageous.

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