|As a research and action organization, Center for Policing Equity (CPE) produces analyses identifying and reducing the causes of racial disparities in public safety. Using data-driven approaches to social justice, we use science to create levers for social, cultural, and policy change.
We are research scientists, race and equity experts, data virtuosos, and community trainers. We use data to build a more fair and just system. We partner with law enforcement and communities. Our aim is to bridge the divide of communication, generational mistrust, and suffering. But most of all, we are the path that science can forge towards public safety, community trust, and racial equity.
DC or NY preferred (Remote for the right candidate)
Center for Policing Equity is looking for a skilled Data Science Developer with a passion for social justice. The Data Science Developer will contribute to CPE’s Data Driven Interventions (DDI) program by developing and maintaining our analytical codebase; providing guidance for team members in statistical coding best practices; and contributing to the development of our ongoing programs with expertise in statistics and statistical coding, all with the goal of more effectively and efficiently developing reports for a quickly-growing roster of police departments that have contributed their data to the National Justice Database (NJD).
The NJD is the first and largest standardized database on police behavior in the country (e.g., vehicle stops, pedestrian stops, use of force, calls for service). The technical reports provided to departments include analyses and data-driven recommendations, with the ultimate goal of reducing racially burdensome policing and empowering police departments to improve relationships with the communities they serve.
Under the direction of the Data Science Manager, the Data Science Developer will primarily work to maintain existing code libraries used by Data Analysts to produce NJD reports; collaborate with Data Analysts to identify enhancements to these libraries; and serve as a team expert in statistical coding best practices. The Data Science Developer is also expected to work in collaboration with CPE’s Product Discovery team to help take novel analytical approaches slated for inclusion into NJD reports from prototype to production. The successful candidate will be able to draw on deep experience as both an applied statistician (with a firm understanding of statistical modeling and it’s real-world applications) and a statistical coder (with a proven track record of implementing statistical code in production). In particular, the candidate will be an experienced user of R, broadly familiar with the latest developments and best practices of coding in R.
- Provide ongoing maintenance of DDI’s R codebase. Identify and implement enhancements and bug fixes.
- Take hand-off from CPE’s Product Discovery (R&D) team to refactor, test, and implement novel analytical prototypes in production.
- Serve as an expert in statistical coding best-practices, providing guidance and mentorship to others on the team
- Advanced degree in Social Science, Statistics, Computer Science or related field
- Advanced training in statistics and practical experience in applied research.
- 3-5+ years of experience writing statistical code, especially for implementation in production environments.
- Expertise in area and able to manage projects / specific scope and also able to make decisions for their day-to-day work
- Familiarity with a broad variety of modeling techniques, including: Bayesian statistics; multilevel (hierarchical) modeling; and time series analysis
- Deep knowledge of contemporary R coding, especially the Tidyverse.
- Professionalization with coding best practices, including code style, package development and unit testing.
- Familiarity with functional and object-oriented programming.
- Experience working with Git/GitHub
- Familiarity with RStudio and related tools
- Experience using cloud computing platforms (such as AWS) to support data analysis
- Familiarity with cleaning and interpreting administrative data
- Strong interest in ongoing learning, staying up-to-date on tools and techniques as well as helping others develop their skills through peer mentorship.
- Comfort with collaboration across a geographically distributed team
- Demonstrates strong critical thinking to solve complex problems
Additional Qualifications (Preferred)
- Experience with Python and pandas
- Familiar with relational (SQL) databases and able to construct complex queries
- Experience working with geospatial (GIS) data