Melissa McNeill is a Data Science Manager for the Crime Lab. In her role, Melissa plans and oversees the implementation of data work, manages and mentors data scientists, and helps establish team processes that promote high-quality data analysis. She is committed to delivering data analyses that are well-organized, technically correct, reproducible, and relevant to partners’ needs. Melissa is a core contributor to Name Match, an open-source probabilistic record linkage tool.
Melissa is broadly interested in using data science to reduce violence and minimize the harms of the criminal justice system. In many of her projects, this involves building and evaluating prediction models that are accurate, fair, and useful in the real world. Her other projects aim to measure the impact of new violence-reduction programs and policies. Melissa holds an MS in Analytics from Northwestern University and a BS in Computer Science from Texas A&M University.
New York City Domestic Violence Analysis
The Crime Lab partnered with the New York City Police Department (NYPD) to identify New Yorkers at high risk of domestic violence to ensure those individuals received social services.
The Crime Lab is working to strengthen public safety datasets by developing Name Match – an open-source, customizable tool to link records from different datasets.