Quantitative Researcher

Verition Fund Management LLC (“Verition”) is a multi-strategy, multi-manager hedge fund founded in 2008.  Verition focuses on global investment strategies including Global Credit, Global Convertible, Volatility & Capital Structure Arbitrage, Event-Driven Investing, Equity Long/Short & Capital Markets Trading, and Global Quantitative Trading.

We are seeking an experienced Quantitative Researcher to join our cross asset systematic trading team. The successful candidate will have over 5 years of experience in commodities, cash equities/equity indexes, or FX and rates, with a focus on mid to low frequency trading strategies. This role involves developing and implementing quantitative models and strategies using Python, with an emphasis on relative value or CTA (Commodity Trading Advisor) type strategies.

Responsibilities:

• Develop, implement, and refine quantitative trading strategies with holding periods ranging from intraday to a few days/weeks. Focus on relative value or CTA type strategies across specified asset classes.
• Perform thorough analysis of market data to identify trading opportunities. Utilize statistical and machine learning techniques to extract actionable insights.
• Implement and back-test quantitative models to validate their effectiveness. Continuously improve models based on performance metrics and market conditions.
• Conduct research to stay up-to-date with the latest developments in quantitative finance, market trends, and emerging technologies. Apply this knowledge to enhance trading strategies.
• Work closely with members of the team and risk management to ensure alignment of strategies with overall business objectives.
• Use Python for the development, implementation, and automation of trading models and strategies. Ensure code is efficient, scalable, and maintainable.

Qualifications:

• 5+ years of experience in quantitative research or trading within commodities, cash equities/equity indexes, or FX and rates.
• Master’s or PhD in a quantitative field such as Mathematics, Statistics, Physics, Computer Science, or Financial Engineering.
• Strong proficiency in Python, including experience with relevant libraries for data analysis and machine learning.
• Deep understanding of quantitative finance, statistical methods, and numerical techniques.
• Proven track record in developing and implementing mid to low frequency trading strategies, with a focus on relative value or CTA type strategies.
• Exceptional analytical and problem-solving abilities, with the capacity to handle complex data and model development.
• Excellent verbal and written communication skills, with the ability to convey complex concepts to both technical and non-technical stakeholders.
• Ability to work effectively in a team environment and collaborate with various stakeholders.