Delivery models for accelerating data science for social good
Delivery models for accelerating data science for social good


The rise of data science has been largely fueled by the promise of changing the business landscape - enhancing one's competitive advantage, increasing business optimization and efficiency, and ultimately delivering a better bottom-line. This promise reaches across sectors as machine learning methods are getting better, data access continues to grow, and computation power is easily accessible. However, because the practice of doing data science can be expensive, there is a danger that this so-called promise of data science may only be available to the most well-resourced organizations with sophisticated data capabilities and staff. For the past five years, DataKind has been working to ensure social change organizations too have access to data science, teaming them up with data scientists to build machine learning and artificial intelligence solutions that aim to reduce human suffering. In doing so, DataKind has learned what it takes to apply data science in the social sector and the many applications it has for creating positive change in the world. This session presents DataKind projects showcasing the wide range of applications for ML/AI for social good. From using satellite imagery and remote sensing techniques to detect wheat farm boundaries to protect livelihoods in Ethiopia, to leveraging NLP to automate the time consuming process of synthesizing findings from academic studies to inform conservation efforts and to classifying text records to better understand human rights conditions across the world to using machine learning to reduce traffic fatalities in U.S. cities, learn about some of the latest breakthroughs and findings in the data science for social good space and learn how you can get involved


A native of Costa Rica, JeanCarlo leads DataKind’s data science practice and ensures that its portfolio of projects are designed to help social change organizations leverage data science to achieve maximum impact. Specializing in the intersection of technology strategy and data science delivery models, JeanCarlo has more than fifteen years of experience in cross-functional roles overseeing and building data science solutions, strategic planning, technology selection and implementation, staffing, coaching, and resource and budget allocation. JeanCarlo started his career as a process engineer in the semiconductor community, after which he spent over a decade in academia. When he is not at DataKind, JeanCarlo can be found teaching Business Analytics at New York University.He holds a B.S. & M.S. in Electrical Engineering and a Ph.D. in Technology Management from New York University

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