Commodities Quantitative Analyst

Company Overview

Verition Fund Management LLC is a multi-strategy, multi-manager hedge fund established in 2008. The firm specializes in various investment strategies including Global Credit, Global Convertible, Volatility & Capital Structure Arbitrage, Event-Driven Investing, Equity Long/Short & Capital Markets Trading, and Global Quantitative Trading.

Role Summary

We are looking to hire a Quantitative Analyst focused on commodities to join our investment pod in Houston. This position is heavily research-driven and involves collaborating directly with an experienced Portfolio Manager to develop innovative investment signals through alternative data sources and quantitative research methods. The role emphasizes market research, hypothesis testing, and development of predictive models rather than software engineering.

Primary Responsibilities

• Create and implement financial time series models and forecasting tools across energy and commodity sectors.
• Source, assess, and integrate alternative data sets such as crude oil vessel tracking (AIS), shipping and freight metrics, pipeline flow data, refinery activities, storage/inventory statistics, weather information, and satellite images into the investment framework.
• Design and validate alpha signals using rigorous statistical techniques and backtesting methods.
• Develop data processing pipelines to manage and analyze both structured and unstructured large-scale datasets efficiently.
• Employ AI and machine learning methods to enhance feature extraction, expedite research cycles, and identify unique investment prospects.
• Work closely with the Portfolio Manager to quickly prototype concepts and iteratively improve models to respond to changing market conditions.

Required Qualifications

• Proficiency in Python and related scientific computing packages including Pandas, NumPy, SciPy, and scikit-learn.
• Strong capabilities in financial time series analysis, statistical modeling, feature engineering, and hypothesis testing.
• Experience with backtesting methodologies, predictive modeling, and evaluation of investment signals.
• Familiarity with machine learning algorithms and contemporary AI tools, particularly large language models, to advance quantitative research.
• Working knowledge of SQL, cloud data platforms, and handling extensive structured and unstructured datasets.