This is a quality assurance and fact-checking role. You will not be building AI systems — you will be stress-testing their outputs against real-world data, with the goal of identifying where and why they fail.

• Source-verify every factual claim against primary sources — exchange data, central bank publications, macro data releases, and price feeds.

• Classify discrepancies by type: incorrect data, outdated information, calculation errors, or misleading framing.

• Maintain a structured audit log documenting each finding, its source, and its potential impact on downstream decisions.

• Escalate material errors promptly, as outputs may inform time-sensitive business decisions.

Secondary Responsibility

After an initial audit period, you will contribute to a summary findings report identifying recurring error patterns, high-risk output categories, and recommendations for improving output reliability.

• Foundational understanding of financial or commodity markets — enough to assess whether a data point is plausible.

• Comfort with basic financial arithmetic: percentage changes, spreads, and unit conversions.

• Methodical research habits — ability to locate and cite primary sources rather than rely on secondary summaries.

Preferred

• Exposure to commodity or FX markets (agricultural products, energy, or major currency pairs).

• Basic Python proficiency — sufficient to run scripts and record outputs in structured formats.

• Familiarity with macro data calendars (central bank meetings, economic data releases).

Not Required

• Prior experience with AI or machine learning.

• Derivatives or options knowledge.