Are you interested in using your software engineering and data science skills for social impact? The Center for Data Science & Public Policy at the University of Chicago has openings for Software/Data Engineers with a strong background in software engineering, databases, machine learning, data science and a passion for social impact. Data Engineers will help build open source software that underlies our projects in education, public health, criminal justice, environment, economic development and international development in partnership with government agencies and nonprofits.
- Lead the development of open source software libraries to enable the research and deployment of our data science projects
- Work with an interdisciplinary team of computer scientists, statisticians and social scientists focused on data science projects with social impact
- Required: Strong programming skills, especially in data-intensive Python using modules such as statsmodels, scikit-learn, pandas, sqlalchemy, etc.
- Required: Database expertise, especially with PostgreSQL. Spatial databases (PostGIS) a plus. Experience with NoSQL databases and analytical cloud-based DBs (Redshift for example) a plus.
- Experience working on real-world problems and passion for making a social impact.
- Experience with data science workflows
- Experience developing code in a team environment using git.
- Experience with Amazon Web Services (EC2, RDS, RedShift, S3, OpsWorks)
How to Apply
Applications should be submitted using this link and include a resume, expected date of availability and contact information for three references. Applications will continue to be accepted until positions are filled.
The Center for Data Science and Public Policy at the University of Chicago is a joint initiative of The Harris School of Public Policy and the Computation Institute that seeks to further the use of data science in policy research and practice. The center consists of data scientists with interdisciplinary backgrounds (computer science, statistics, math, physics, economics, social sciences), policy researchers, and practitioners who work together with external partners to solve problems with social impact. Over the last two years, we have focused on projects such as:
- Identifying which children are most likely to get lead poisoning so public-health officials can remove the hazard before the children get poisoned (Chicago Department of Public Health)
- Predicting which students are most likely to drop out of school so educators can intervene before it happens (with schools districts in MD, AZ, VA, and NC)
- Improving early-warning indicator systems for police departments to help officers get the training, resources, and counseling they need to avoid adverse interactions with the public (with The White House and various Police Departments)
Our research includes supervised learning for large-scale behavior prediction, text analysis, signal processing and speech analysis, experiment design, online and active learning, and combining machine-learning techniques with social science and behavioral psychology.
Learn more at http://dsapp.uchicago.edu/.