What does data science look like at the Rockefeller Foundation? What does a typical day look like for the team?
Data has actually been at the core of the Rockefeller Foundation’s approach to realizing impact since its founding in 1913. Taking a hypothesis- and data-driven approach was called “scientific philanthropy” at that time. The Rockefeller Foundation Statistics and Machine Learning team was originally envisioned to take advantage of the newest analytical techniques to enhance our data-driven approach to our existing philanthropic activities while adding data science as a consultation service or tool that we could offer alongside monetary resources.
Building on that vision, the team now sits under the broad umbrella of the Innovation Team and is only about two years old. While that might sound like a while, the reality is that we are still figuring out what we want data science in philanthropy to look like for the Foundation — having a stable source of funding and a mission to improve the well-being of humanity creates an incredibly privileged space to test, fail, and imagine in our work.
Today, the team is made up of three full-time data scientists as well as a group of consultants who support us; we do everything from supporting data-related inquiries at the Foundation to leading independent projects that are aligned with the work of our initiatives.
Because our work can vary so much, there is no typical day — I’ve had days crunching with consultants to get materials ready for an Initiative’s project launch, but also days without a single meeting where I am debugging my model. Currently, our tech stack includes Domino as well as specific platforms for niche work — for example, I use Google Earth Engine primarily for my remote sensing work.
How does the team select and proceed with projects to work on?
There are two main avenues through which we identify projects: through a proposal or request from an internal initiative or through an idea from within our own team. After discussing a potential project internally and with trusted partners, and if the project is approved, we move forward with a projected timeline and budget. From there, every project is very different.
For projects that we are pitched on or asked to help on, we usually jump in at a phase where we know the work is a reasonably good idea and we pass the results off immediately — these projects might even take less than a day.
For the projects that we envision and develop…
Continue reading: https://towardsdatascience.com/adapting-data-science-tools-for-social-impact-in-philanthropy-73a8a382c79c?source=rss—-7f60cf5620c9—4