Senior Data Scientist

Responsibilities

• Be responsible for crafting new data project plans and methodological approaches, strategizing incorporation of outside datasets, and managing the data team along with internal collaborations to maximize impact on policy and practice.
• Articulate, in consultation with the Project’s Faculty Directors, a strategic vision for the expanded collection, processing and analysis of carceral morbidity data.
• The Senior Data Scientist (Supervisor) will supervise the team’s Data Analyst, Data Scientist, Senior Research Scientist, and several student research assistants.

Skillset

• Master’s Degree in Data science, quantitative social science, or related field OR 5+ years working experience as a data or research scientist or equivalent combination of education and experience.
• Substantial research experience.
• Prior building or maintaining productionized ETL pipelines in R or Python.
• Experience planning and executing quantitative research including data collection, cleaning, analysis, and publication.
• Experience working with datasets that include missing, unstructured, and/or incorrect data.
• Experience establishing relationships and working with diverse collaborators (e.g., local advocates, government officials, and journalists) with varying levels of technical literacy.
• Strong organizational skills for project planning, data collection procedures, and complex research activities.
• Experience with public record requests and creating impactful data visualizations for social media and news reports.
• Proficiency using R or another programming language to manipulate data and draw insights from large, complex datasets.
• Strong writing skills, including experience with peer-reviewed manuscripts.
• Experience using git/GitHub to track tasks and data acquisition processes, and regularly update data on GitHub.
• Experience creating impactful data visualizations for social media and reports.
• Experience developing open-source datasets and GIS platforms.
• Experience planning and executing quantitative data projects including collection, cleaning, analysis, and archiving.
• Experience supervising similarly sized data teams and working with diverse collaborators with varying levels of data literacy.