A Data Science Team for the Social Sector
April 06, 2015

Seeking visionary partners to address critical issues affecting public health, the environment and cities.

How can we ensure that people around the world have access to healthcare services they need? How can we protect the environment and halt climate change? How can we ensure cities are thriving and healthy? To move the needle on tough issues like these, we're going to need everybody's help.

As part of DataKind's mission to harness the power of data science in the service of humanity, we launched a new initiative to tackle humanitarian challenges sector-wide.

Announced at Clinton Global Initiative in 2014, our In-House Team works with collective networks of nonprofit, foundation, corporate and government partners on year-long projects to develop cutting-edge solutions for society’s most pressing challenges.  We're now building a team of partners to join us - apply now through May 1 by emailing Erin Akred for a free consultation to help assess your organization's needs.


Are you...

  • collecting data about your programs, beneficiaries or issue (or partnered with an organization collecting data)?

  • dedicated to evidence-based decision making and measurable impacts?

  • known in your field for innovation, collaboration, rigor and impact?

  • interested in using data for more than advocacy?

  • ready to learn from your data, no matter how hard the truths?

  • eager to partner with multiple stakeholders working to promote public health, the environment and thriving cities


How We Can Help

By analyzing wide-ranging data sources, we can help your organization with a variety of needs. Whether you're trying to understand the discrepancy between supply and demand of medical services or track the spread of a disease or the erosion of a coastline, uncover patterns in urban consumption or effective conservation efforts , by working with us, your organization will:

  • learn where data can provide insights and see how being “powered by data” can help you do even more

  • partner with a small, dedicated, passionate and expert team of data scientists

  • benefit from our quantitative project management skills to move you from challenge to project plan to solution

  • become aware of open data resources that will enrich the data you already have

  • collaborate with other organizations working in your field to develop sector-wide solutions

  • connect with our global network of data science volunteers and technology partners  

See What's Possible

DataKind has matched and managed data science practitioners and social change organizations on more than 45 data science projects designed to address critical humanitarian issues. These three examples showcase our efforts in the issue areas of public health, the environment and cities - see more examples of our work here.


Our partner in Haiti and Kenya, NexLeaf, monitors the cold supply chain for vaccines using cell phones equipped with temperature sensors.  The cell phones are placed in transportation storage units and in refrigerators in health clinics that house vaccines to monitor conditions under which the inventory is maintained.  Based upon the data NexLeaf was generating and the subject matter expertise of on-the-ground practitioners, DataKind data scientists advised on which questions could be effectively answered by the data.  To answer those questions, the team applied predictive and other pattern recognition algorithms to their dataset, ultimately disproving some assumptions and identifying remedies to address other newly discovered weaknesses in the supply chain.  Insights were made available to on-the-ground partners to prevent vaccines from spoiling, thereby potentially saving lives.


NYC Parks

NYC Parks Department has been capturing extensive data on its pruning program on city streets for a number of years. The department wanted to know if preventative tree care might reduce the number of hazardous tree conditions in the future and whether one could predict where trees will be most vulnerable to a storm and plan accordingly to minimize post-storm work.  Over the course of a weekend, DataKind data scientists made a number of discoveries and showed that prudent pruning reduces emergency cleanups by 22 percent. The group also built an interactive map that quantifies potential storm risk for planning purposes.



GiveDirectly supports the poorest of the poor through direct mobile cash transfer. It has found that a simple yet effective proxy for poverty in the areas where it operates is the type of housing materials a family uses for its home, which has a strong correlation to poverty levels in national survey data. GiveDirectly asked: Is there a way to target villages in need—a time-consuming manual process—by using data and technology? DataKind data scientists created an image processing machine learning algorithm that would estimate whether a house had a thatch or metal roofing based on Google satellite imagery.  

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