Responsibilities
Develop financial time series models and predictive forecasting techniques across energy and commodity markets.
Research, evaluate, and incorporate alternative datasets into the investment process.
Design, test, and validate alpha signals through rigorous statistical analysis and backtesting.
Build research pipelines to clean, organize, and analyze large structured and unstructured datasets.
Leverage AI and machine learning techniques to improve feature engineering, accelerate research, and identify differentiated investment opportunities.
Collaborate with the Portfolio Manager to rapidly prototype new ideas and continuously refine investment models as market dynamics evolve.
Qualifications
Strong proficiency in Python and the broader scientific computing ecosystem, including Pandas, NumPy, SciPy, and scikit-learn.
Experience with financial time series analysis, statistical modeling, feature engineering, and hypothesis testing.
Knowledge of backtesting frameworks, predictive modeling, and signal evaluation techniques.
Experience applying machine learning techniques and modern AI tools, including large language models, to quantitative research workflows.
Proficiency with SQL, cloud-based data platforms, and working with large-scale structured and unstructured datasets.
Interest in commodities, global markets, and alternative data research.
Excellent written and verbal communication skills.
High level of intellectual curiosity, strong work ethic, and a keen attention to detail.
Ability to work effectively in a team-oriented, fast-paced, and dynamic environment
