Extract structured variables from unstructured legal text datasets using regular expressions (regex) and other text-parsing techniques.
Manually review extracted variables to ensure accuracy, correcting any misclassifications or incomplete extractions.
Develop and apply validation tests to systematically check that extracted data aligns with legal definitions, case contexts, and expected formats.
Iterate on extraction rules based on observed patterns, edge cases, and evolving dataset needs, refining regex and other text-processing approaches.
Maintain detailed documentation of extraction logic, validation procedures, and any manual review criteria for transparency and reproducibility.
Collaborate with analysts and researchers to align text extraction with broader research goals, ensuring outputs are fit for downstream analysis.
Identify and address anomalies by systematically checking for missing, incorrect, or inconsistent values in extracted datasets.
Automate parts of the extraction process where possible, while ensuring that human review remains a core quality-control step.
Ensure zero-tolerance for errors in final outputs through a combination of automated checks, peer review, and thorough manual validation.

Application Requirements:

Familiarity with specific software, tools, and languages:
Proficient in R, including dplyr, stringr, and other relevant text-processing packages.
Fluent in leveraging ChatGPT, Claude, and similar AI tools for troubleshooting, automating data extraction, and refining regex-based methods.
Fluent in using GitHub.
R scripts / github codebase for review

Scrutinize jobs