CVR Data Analysis for Elections
Budget: $250 – $750 USD
I need a detail-oriented analyst to examine Cast Vote Record (CVR) files with the aim of strengthening voter-integrity efforts. My goal is to understand irregular patterns in registration, turnout, and ballot tabulation while keeping the work strictly non-partisan yet grounded in a traditionally conservative approach to election transparency.
Scope of work
• Import and clean raw CVR files (multiple counties and election cycles).
• Run statistical tests and anomaly detection to flag outliers in ballot style, precinct, and batch results.
• Cross-reference CVR findings with publicly available voter-registration and turnout data to validate consistency.
• Summarize findings in a clear narrative report supported by charts or dashboards (Excel, Tableau, or Python-based visualizations—whichever you prefer).
• Provide all processed data, code/notebooks, and a brief methods document so results can be replicated.
Acceptance criteria
• Reproducible workflow with commented scripts or documented steps.
• Report highlights any significant discrepancies, explains methodology, and includes visual evidence.
• Data privacy respected; no personally identifiable information exposed.
Experience with election data, statistical modeling, Python or R, and visualization tools will be highly valued. If this fits your skill set, let’s discuss timelines and file formats so we can move forward quickly.
Scope of work
• Import and clean raw CVR files (multiple counties and election cycles).
• Run statistical tests and anomaly detection to flag outliers in ballot style, precinct, and batch results.
• Cross-reference CVR findings with publicly available voter-registration and turnout data to validate consistency.
• Summarize findings in a clear narrative report supported by charts or dashboards (Excel, Tableau, or Python-based visualizations—whichever you prefer).
• Provide all processed data, code/notebooks, and a brief methods document so results can be replicated.
Acceptance criteria
• Reproducible workflow with commented scripts or documented steps.
• Report highlights any significant discrepancies, explains methodology, and includes visual evidence.
• Data privacy respected; no personally identifiable information exposed.
Experience with election data, statistical modeling, Python or R, and visualization tools will be highly valued. If this fits your skill set, let’s discuss timelines and file formats so we can move forward quickly.