Impact | Data Science
From 2023-2024, Jennifer worked with a team of data scientists to provide actionable data tools and analytics for a regional affordable housing non-profit. The non-profit connects renters with affordable housing options via its user-friendly website. The team evaluated how well Massachusetts’s available affordable housing stock met the demands of in-need renters.
First, the team helped translate the non-profit’s definition of good local housing stock - ample and attainable - into an evaluative KPI. Using census data and the organization’s inventory of housing units, they scored each town between 0 and 1.
Next, they created a graph network to indicate if website users' income criteria, accessibility needs, and living preferences aligned with rental options in their geographic search areas. By creating and spatially visualizing a network, the team could efficiently study the strength of the “match” between searchers’ current hometowns and desired hometowns.
Analyses like these can inform:
High-impact advocacy campaigns targeting developers, policy-makers, and community-based organizations.
Strategic growth roadmap
Web development feature roadmap
Grant application and reporting insights
Tooling | Sustainability | Data Science
Jennifer has an ongoing relationship with environment tech nonprofit WattTime, where she work on strategic open-source extensions of their SDK. Recently, she has been finalizing an optimization module in Python that outputs a smart device charging schedule based on WattTime’s local electricity emissions forecast. Initial research suggests that when data customers integrate this free tool into their products, they could automatically avoid up to 30% of emissions.
Supplemental data science support can help with the following:
Building optimization algorithms for real-world applications
Working with environmental and open data in APIs and SDKs
Creating developer-friendly open-source tools
Balancing environmental impact with user needs