Justice Innovation Lab (JIL) provides data-driven, evidence-based analysis to prosecutors to reduce incarceration and racial disparities and increase fairness in the criminal justice system. JIL is seeking a senior data scientist and technologist to serve as the Director of Analytics and to lead the technical and analysis components of JIL’s efforts in data-driven prosecution reform.
The Director of Analytics will be responsible for leading and directing all aspects of JIL’s data analysis program, including developing a strategic plan for analyzing prosecutorial data, supervising and managing teams of data scientists and technologists (both internal and external to the organization), and deriving insights from criminal justice data sets.
Using data from law enforcement partners, JIL will analyze criminal justice data to assess performance on key criminal justice metrics. Such metrics may include: time-to-disposition; dismissal rates; racial disparities in pretrial detention, plea bargaining, and sentencing; pretrial detention rates; revocation rates; diversion completion rates; application of fines and fees; charge reductions; and sentences of incarceration for nonviolent crimes. The goal of these analyses is to identify and redress root causes of disparities and unnecessary prosecutions. JIL, in conjunction with our partners, will work to design data-based technical solutions and policy changes to address identified problems. Future projects might include incorporating predictive analytics regarding prosecutorial decision-making.
The Director of Analytics will work closely with the Executive Director to determine the best strategies for analyzing data and for proposing technical solutions to prosecutors, which may include creating new data collection systems, designing applications and/or internal or public facing dashboards, and/or building machine learning interventions to assist prosecutors with making fairer decisions.
Responsibilities and Tasks
- Lead JIL’s data analysis projects
- Identity policy changes beneficial to prosecutors seeking to reduce bias and unnecessary prosecutions
- Work with partners to design and create dashboards, maps, or other data visualizations
- Collaborate with internal and external partners to evaluate the effectiveness of criminal justice reform initiatives
- Coordinate and manage all data scientists and researchers working on JIL’s projects
- Lead and oversee the development of technical solutions for data collection and data analysis
- Cultivate partnerships with universities and technology start-ups to enhance the organization’s technological capacity
- Responsible for the collection and storage of criminal justice data, and managing agreements with outside partners, to ensure compliance with best practices
- Draft or author articles, social media posts, website content, or other documents related to data analysis and criminal justice reform
- Participate in and support regularly scheduled conference calls with partners and collaborators
- Participate in relevant academic and professional conferences
- All employees of NVF are required to complete timesheets
Education, Experience, Knowledge, Skills and Ability
At a minimum, candidates must possess a master’s degree in data science, statistics, economics, or a related field and two years of work experience in statistics/analytics, or a Ph.D. in a similar field, or similarly extensive work experience.
Ideal candidates are comfortable with a start-up work environment and strive to tackle social challenges greater than themselves. Background, prior experience or prior course work in criminal law and/or criminal justice is strongly preferred. Knowledge of ongoing efforts in areas such as policy innovation and data for social good is a plus. Experience working in or familiarity with government digital service, data, design, technology and/or innovation is also a plus.
The following qualifications are required:
- Expertise with Python, R, Stata, or comparable data science skills
- Strong interest in common program languages, data visualization software and mapping tools
- Considerable experience evaluating program effectiveness and using data for continuous program learning
- Ability to use descriptive statistics, predictive analytics, machine learning, and other methods to learn about and derive insights from patterns and relations within a dataset
- Significant project management experience, from the inception and design phase through to full implementation
- Relevant experience managing teams involved in complex data analysis and/or development of technological solutions
- Experience building relationships and initiating activities across stakeholder organizations and individuals
- Ability to present complex data analysis in a visual appealing way, such as developing dashboards
- Ability to automate data pulls, manipulations, and other rote activities to enable efficient agency operations
- Commitment to ensuring work product adheres to methodological and open science best practices
- Comfort sharing interim work products, seeking feedback, and working collaboratively
- Ability to work independently to solve problems
- Strong organizational, writing, analytical, speaking, and interpersonal skills, and attention to detail
- Demonstrated commitment to using data and technology for public good.
- Curiosity and interest in public policy and public interest work, particularly in the criminal justice realm
- Excellent communication skills, including with people who do not have an analytics background
- Comfort working in a fast-paced, deadline-oriented environment
- Self-starter with a desire to design and launch new initiatives
The following qualifications are desired:
- Prior experience working with lawyers and/or in the criminal justice sector
- Experience working in or familiarity with government digital service, data, design, technology and/or innovation
- Demonstrated ability to produce research outputs in multiple formats and to tailor writing to multiple audiences
- Experience in experimental design, including issues of sampling, randomization, and statistical inference.