Data Research Intern

Summary

Climate X is a leading climate risk intelligence company delivering high-resolution physical risk insights globally. Our product suite empowers organisations to measure, understand and mitigate the impact of climate change on physical assets and operations. We’re hiring a dedicated Data Quality Analyst to validate outputs and ensure the highest standards of data quality.

• Review and validate asset location data against online sources.
• Identify inaccuracies, inconsistencies and edge cases requiring deeper investigation.
• Conduct structured QA audits following established workflows and checklists.

• Troubleshoot anomalies and determine root causes of data irregularities.
• Interpret ambiguous model outputs and apply sound judgment when verifying data.
• Provide clear, structured feedback to improve pipeline logic and validation processes.

• Follow defined QA processes, including documentation, peer review and escalation paths.
• Suggest refinements to validation workflows and contribute to operational efficiency.
• Maintain comprehensive and clean issue tracking.

• Use AI tools (e.g., ChatGPT) to support validation and cross-checking.
• Use Excel for data manipulation, including Pivot Tables and lookups (Power Query a plus).

• Communicate clearly with Product and Engineering teams about issues, risks and trends.
• Manage stakeholders across the business when delivering customer data.
• Participate in rapid feedback loops as the product’s logic and capabilities evolve.

• Experience in data validation, data QA, anomaly detection or related operational roles.
• Experience performing research tasks.
• Strong analytical skills and high attention to detail.
• Proficiency with Excel (Pivot Tables, lookups; Power Query beneficial).
• Familiarity with AI tools such as ChatGPT or similar.
• Excellent written communication skills.
• Ability to follow structured workflows and maintain documentation quality.

• Experience validating or reviewing data.
• Background in data operations involving complex or evolving datasets.
• Exposure to GIS tools (QGIS, ArcGIS) or willingness to learn.