Deploying Machine Learning Models and Working at One of the World´s Leading NGOs
How Bruno grew from Omdena collaborator to deploying ML at a leading NGO, scaling data-driven solutions to reduce poverty globally.
Bruno Ferreira da Paixão from Brazil joined Omdena as a collaborator and later became an Omdena Top Talent, which opened doors to paid project opportunities. Through this experience, he was hired by Catholic Relief Services to help scale machine learning solutions that support poverty reduction efforts across multiple countries.
Bruno, what is your career background?
I’ve always been passionate about using data to drive decisions in public policy. My career started in information technology, and over time, I focused on project management, deploying machine learning solutions, leading teams, and improving systems in the public sector.
“Deploying machine learning models is much more than writing code. It means understanding the problem, designing the right solution, and delivering real value to the organization.”
How did you join Omdena, and why?
I joined Omdena in 2019 by applying to one of the collaborative AI Challenges. I was drawn to the idea of using artificial intelligence to solve real-world problems and create social impact.
Projects Bruno worked on through Omdena:
- Creedix – Machine Learning for Credit Scoring: Banking the unbanked
- Impact Hub Istanbul – Disaster Response: Improving the Aftermath Management of an Earthquake
- SexedPL – Analyzing the Importance of Sex Education
- Red Dot Foundation (Safecity) – Preventing Sexual Harassment With Artificial Intelligence
- UNHCR – Predicting forced displacement in Somalia
Source: Bruno having fun at his workstation
How did Omdena help your career?
Working with real datasets and real community challenges made a huge difference. It gave me hands-on experience building solutions that matter.
I’ve always believed that data should be used to improve people’s lives. Through Omdena, I was able to apply AI to meaningful problems and grow professionally while contributing to social impact.
What is one technical solution you recently worked on?
In a recent project in Japan, we created a risk zone scoring model using data on floods, earthquakes, and distance to hospitals. This helps communities understand environmental risks and prepare more effectively.
This hands-on way of working aligns with how AI is now being used to reduce NGO grant writing time by 50%, proving that well-designed AI tools can directly free up capacity for mission-critical work.
I believe this is a strong example of how AI can directly support public safety and improve quality of life.
Source: Project dashboard screenshot
