Portfolio Manager, Systematic Agriculture

Teza Technologies•Austin, TX

2d•Onsite

About The Position

We're looking for an experienced Portfolio Manager to build and scale systematic trading strategies across global agricultural markets. This role is ideal for someone who combines deep knowledge of agricultural commodity fundamentals with a quantitative mindset. You'll have the opportunity to develop differentiated alpha by integrating supply and demand dynamics, weather, satellite imagery, logistics, positioning, and alternative datasets into systematic trading models. You'll work alongside experienced quantitative researchers, engineers, and data specialists while having the autonomy to shape research direction, portfolio construction, and execution.

Requirements

Deep understanding of agricultural commodity markets, including supply and demand dynamics, seasonal effects, logistics, weather, and global trade flows.
Several years of experience managing or researching systematic commodity strategies.
Strong quantitative background with experience applying statistical or machine learning techniques to financial markets.
Excellent Python programming skills and the ability to work with large datasets.
Experience building systematic investment strategies rather than discretionary trading alone.
Strong understanding of portfolio construction, risk management, and systematic execution.
Intellectual curiosity and a passion for developing new sources of alpha.

Nice To Haves

Experience trading multiple commodity sectors beyond agriculture.
Familiarity with alternative datasets, remote sensing, satellite imagery, or geospatial analytics.
Experience with machine learning applied to commodity forecasting.
Knowledge of real-time research infrastructure and production trading systems.
Graduate degree in a quantitative discipline.
Track record of managing external or proprietary capital.

Responsibilities

Develop and manage systematic trading strategies across agricultural commodity markets.
Generate alpha by combining fundamental commodity research with quantitative modeling and statistical analysis.
Build predictive models using datasets such as weather, crop conditions, satellite imagery, freight flows, inventories, positioning, macroeconomic data, and other alternative data sources.
Design and evaluate new signals through rigorous research, backtesting, and performance attribution.
Continuously improve portfolio construction, risk allocation, and execution efficiency.
Partner closely with quantitative developers to productionize research and build scalable trading systems.
Work across the full research lifecycle, from idea generation through live deployment and ongoing monitoring.