Research Integrity Intern
ExternalS$10K–S$14K/yrContractUnknownToday
Machine LearningPython
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Responsibilities
- Primary Responsibility
- 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- Classify discrepancies by type: incorrect data, outdated information, calculation errors, or misleading framing.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.
Requirements
- Required
- 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.
Additional Information
Agrocorp International is exploring the use of AI-assisted tools to support internal research and analysis workflows across commodity markets. As part of this initiative, we are looking for a detail-oriented Research Integrity intern to assist in evaluating the quality and accuracy of AI-generated research outputs. 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.
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