Job Purpose and Impact
The Senior AI Researcher will apply frontier generative-AI methods and globally integrated market perspectives to discover new trading signals in areas where traditional quantitative methods have had limited success, promoting the company's competitive position and ultimately growing trading earnings. In this role, you will design and run rigorous research experiments — spanning fine-tuning, reinforcement learning, world models and other advanced approaches — to test which hypotheses generate durable signal. You will work across product lines to evaluate complex new datasets and novel sources of data, and partner with the AI Research Lead to move promising research from hypothesis to production.
Key Accountabilities
- Design, run and evaluate generative-AI research experiments (fine-tuning, reinforcement learning, world models, agentic/reasoning approaches) to identify and validate new trading signals.
- Validate quality and coverage of datasets and proposes new datasets to be captured working with other analytical teams and leadership to identify and prioritize broadly defined data and analytical tool development efforts based on trading opportunity.
- Build reproducible research pipelines, benchmarks and evaluation frameworks to rigorously measure signal quality, robustness and overfitting risk.
- Partner and consult with regional and global analytical teams to ensure we gather, centralize and use the most valuable data for analysis in a systematic, repeatable and accurate manner.
- Document research findings, methods and best practices, and clearly communicate results and recommendations on what should be productized.
- Stay at the frontier of external GenAI research (papers, models, techniques) and bring new possibilities into the trading research agenda.
- Independently handle complex issues with minimal supervision, while escalating only the most complex issues to appropriate staff.
- Other duties as assigned
Qualifications
Minimum Qualifications
- Bachelor's degree in a related field or equivalent experience
- Ability to understand business requirements and serve as a consultant to internal teams
- Experience with agricultural trading business and an understanding of the information that adds value to trading
- Significant experience with and understanding of relational and distributed data systems
- Experience using data visualization applications to develop and deploy insights and reporting
- Demonstrated experience conducting applied machine-learning / generative-AI research, including model fine-tuning and/or reinforcement learning
Preferred Qualifications
- Ability to develop queries using multiple programming languages
- Familiarity with scientific programming languages
- Minimum of 10 years of experience working with data and data warehousing systems
- Advanced degree (MS/PhD) in machine learning, computer science, statistics, physics or a related quantitative field
- Publications or demonstrable research in generative AI, deep learning, reinforcement learning or sequence / time-series modeling
- Hands-on experience fine-tuning LLMs and building evaluation harnesses
- Experience with world models, RL and/or self-supervised learning applied to noisy, non-stationary domains
- Strong Python skills and modern ML frameworks (PyTorch, JAX)
- Experience applying GenAI / ML to financial or trading signal generation
- Understanding of commodity and financial markets and sources of alpha
