Role OverviewVerition is seeking a Quantitative Analyst to join a commodities-focused investment pod in Houston. This is a highly research-oriented role working directly alongside an experienced Portfolio Manager to develop differentiated investment signals using alternative data and quantitative research techniques. The successful candidate will combine a strong foundation in statistics, financial modeling, and Python with a genuine curiosity for uncovering new sources of alpha.
Rather than focusing on software engineering, this individual will spend their time researching markets, identifying unique datasets, testing hypotheses, and developing predictive signals that can be incorporated directly into the investment process.
A significant portion of the role will involve sourcing, analyzing, and modeling alternative datasets related to global commodity markets. This includes working with data such as crude oil vessel tracking (AIS), shipping and freight activity, pipeline flows, refinery operations, storage and inventory data, weather, satellite imagery, and other non-traditional datasets. The objective is to transform raw information into robust, statistically validated signals that provide a measurable investment edge.
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
