“I’ve worked with AI for a long time, and it’s great to apply the technology for the good of the
community” – Nagib Hakem
"Working alongside Nagib was an invaluable experience. His mentorship was truly transformative, and
his expertise elevated DevOps work here at LifeMoves. Nagib’s ability to seamlessly bridge complex data
insights with real-world impact made a lasting difference. He was not just a colleague but a mentor
whose guidance was priceless—a true gem in every sense." – Carmen Kapanga, Compliance Data
Analyst at LifeMoves
At his previous employer Nagib was a Principal Engineer, a very technical position focused on AI
systems, with a particular interest in knowledge representation and reasoning. When he retired, he
went on the Camino de Santiago trip for a month before starting this volunteering effort. With a fresh
mind and a new perspective on life, he was ready to apply his skills in the nonprofit world.
LifeMoves is a nonprofit organization based in the Bay Area, and one of the largest and most effective
providers of shelter/housing and supportive services across Silicon Valley and the San Francisco
Peninsula. It operates over 26 major sites, providing emergency, transitional, and permanent supportive
housing, along with a vast array of supportive resources to help clients secure a place to call home.
LifeMoves served over 7450 clients in FY24 (July 1, 2023 – June 30, 2024), and with very good results. Over the past three years, over 80% of clients who secured permanent housing after staying at
LifeMoves kept that housing one year later.
LifeMoves had collected over 20 years of data in Salesforce but didn't know if this data could be useful in
understanding donor behavior and engagement. This is critical for any nonprofit because better
knowledge of donors can significantly impact fundraising efforts, ultimately allowing the organization to
serve more homeless people and provide more homes.
Given his background, Nagib knew he could help, making this a perfect match. The goal was to infer
from the data how donors would behave, and what excites them. They set out to develop a system to
achieve this.
First, he had to analyze the data from Salesforce. A challenge was that he was the sole engineer. When
he encountered unfamiliar aspects of Salesforce, he relied on the extensive online community for
answers. Afterward, he developed a machine learning model, a form of AI, and interfaced it with
Salesforce to write the results back into the database.
The analysis itself was not too difficult; the real challenge was making it accessible to non-technical
users. It needed to be robust so that the team could continue to benefit from it after he left. Everything
is configurable, allowing them to update and use the configuration files as the data evolves.
Initially, they set out to explore possibilities without expecting the high accuracy they ultimately
achieved. The effort culminated in the development of a complete package, which was a pleasant
surprise for both LifeMoves and Nagib.
The culture at LifeMoves was different from the tech industry, where the focus is heavily on technology
and results. At LifeMoves, and likely in much of the nonprofit world, there is a stronger emphasis on
people. The diverse group included employees who had themselves experienced homelessness, creating
a welcoming, open, and community-focused environment.
One of the most rewarding aspects of his fellowship was mentoring some of the LifeMoves employees
on data analytics. One of the by-products of his work was changing the mindset around how they use
data. There is so much information we can get from large amounts of data, it needs to be considered
differently from the day-to-day use of the system.
So far, they have done a 1st pass at predicting donor behavior, focusing on donor retention. The team is
trying to figure out how to improve the methods for the fundraising campaigns based on the newly
developed system. Now, every donor has an insight card in the database, so they can always look up
their history and their various giving propensities. They keep discovering new factors – seeing what
makes donors remain active, for example, and they are refining our campaign strategies accordingly.
This has been a very rewarding time, not only from a technical perspective, but interacting with the
LifeMoves team. Now Nagib is trying to ask himself what he wants his retirement to be and whether
he’s ready for retirement, or just take on another project.
