Commodities Quantitative Researcher

A leading Hedge Fund is looking for an experienced Quant Researcher to join their collaborative, and entrepreneurial systematic investment team. They are seeking a strong commodities quantitative researcher to join in developing new signals and strategies. This opportunity provides a dynamic and fast-paced environment with excellent opportunities for career growth.

Job Description

Quantitative Researcher as part of a small, collaborative systematic global macro team with a focus on applying cutting edge techniques to strategies across the commodities, fixed income, and FX space.

Location

London or Switzerland

Principal Responsibilities

• Identify and onboard new global markets and datasets
• Analyze and manipulate large and diverse data sets for idea generation and alpha research with a focus on the commodities space
• Research and develop signals, leveraging a variety of market and fundamental datasets, to be deployed in systematic trading strategies
• Collaborate with the Senior Portfolio Manager and team in a transparent environment, engaging with the whole investment process from idea generation through execution with a focus on the Global Commodities space

Preferred Technical Skills

• Strongly skilled in Python
• Experience programming in C is a plus
• Bachelor, Master’s, or PhD degree in Computer Science, Engineering, Applied Mathematics, Statistics or related STEM field
• Excellent communication, analytical, and problem-solving skills

Preferred Experience

• 2-4 years of experience working in a quantitative research capacity with a focus on Ags, Energy, Metals, Fixed Income, FX, or Equity Index strategies
• Experience working with large and diverse data sets, specifically data sets associated with the commodity markets
• Commodities market experience modeling futures curves and/or cross-market relationships

Highly Valued Relevant Experience

• Experience in quantitative, econometrics, asset pricing, or macro sub-fields
• Experience with machine learning, statistical techniques and related libraries