Available Positions

Data Scientist / Programmer Research Assistant

Data-Driven Lab is seeking to hire a Yale or National University of Singapore (NUS) graduate student as a data scientist/programmer. Candidates will contribute to a range of projects that aim to bring quantitative rigor and analysis to environmental policymaking. Research projects include building databases and utilizing data science techniques to evaluate a range of environmental issues and policies, including urbanization, climate change, energy, and air pollution. Our work has been published in high-profile academic journals, including Nature and Nature Climate Change,and has been featured in popular media, including The Economist, The New York Times, The Atlantic, andScientific American, among others.

A full-time (37.5 hours/week) position, based at Yale-NUS College in Singapore, is available for the summer months, beginning in May 2019 (travel costs to Singapore and lodging at Yale-NUS College are provided if ideal candidate is coming from overseas). There is also a possibility to extend the role into a remote, part-time position during the academic year. During the academic year, student research assistants work an average of 5-10 hours each week, but can work up to 19 hours a week.

Background of Data-Driven Lab

Data-Driven Lab uses cutting edge data analytics to develop solutions to the world’s environmental problems. Launched in 2015, the research group is an interdisciplinary collaboration of policy experts, data scientists, visual designers, and programmers. For more information about our projects, see www.datadrivenlab.org.

Position details

Data Scientist

We’re looking for students with data science skills who are interested in using statistics and programming to assist with a range of tasks, from statistical modeling to development of front-end data visualizations and graphics. In the past, we’ve developed interactive infographics, high-resolution maps, and data portals, among other projects. Ability to code in R and/or python is a must.

We’re looking for students who can:

  • Scrape, analyze and visualize data using R or Python;
  • Develop inferential statistical models in R or Python;
  • Program R packages.

Programmers

We’re looking for students with data science skills and/or practical programming experience to assist with a range of tasks, from big data mining to development of front-end data visualizations and graphics. In the past, students have worked with us to help build databases, scrape public data sources, and develop machine learning models, and developed interactive infographics, high-resolution maps, and data portals, among other projects.

Here are some of the skills we’re looking for:

  • Scrape, analyze and visualize data using R or Python;
  • Develop inferential statistical models in R or Python;
  • Statistical analysis, data visualization, and web scraping in Python;
  • Presentation of data and research findings in attractive, web accessible visualizations (RShiny, Javascript/d3);
  • Presentation of data and research in map-based formats (Tableau, CartoDB, Javascript/Leaflet)
  • Website development (PHP/WordPress);
  • Work on open-source geospatial calculation tools utilizing GIS (QGIS or ArcGIS) in a Bash environment utilizing command-line tools (GDAL/OGR/GRASS);
  • Development and maintenance of database tools written in open source languages (PostgreSQL, SQLite, etc.).

Strong organizational, interpersonal, communication, and analytical skills are also required. The ideal candidate is a self-starter and independent worker, who can also collaborate closely with other team members to develop and implement projects. We also expect the ideal candidate to oversee and mentor Yale-NUS College undergraduate research assistants who will also be working with our group this summer.

If any of the above fit your skills and interests, we encourage you to apply, by sending a resume, cover letter, and code samples (e.g., a Github page) to amy.weinfurter@yale.edu, with the subject “Data-Driven Lab Data Science RA.”

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