Passionate about turning data into actionable insights and building ML systems that create real impact.
I specialize in building machine learning systems that don't just perform, they serve. From credit risk models that catch defaulters before they ghost, to grant-matching engines that save organizations hours of human effort, I design solutions that work in the real world, under real constraints. My toolbelt includes Python, Scikit-learn, XGBoost, and a fondness for Dockerizing everything I can get my hands on. Whether it's fine-tuning GPTs or wrangling 70,000+ rows of grant data, I engineer with purpose, not just pipelines.
I'm passionate about sharing knowledge and building community. I've spoken at DevFest Ilorin 2024, Women Techmakers, and Google Developer Student Clubs, reaching 100+ participants. I also contribute to open-source projects like NetworkX, managing 30+ pull requests.
Sharing knowledge through articles, workshops, and conference talks. Read more on Medium.
Looking at how I started and how my growth has been honestly exponential, I have God ofc to thank and my love for projects. Best believe if I learn how to spell 'A' I will enter a spelling bee competition. I constantly set myself up (I regret it sometimes) which has made...
Around July last year, I got a role as a machine learning engineer, and since then, I paused my writing, but now I am ready to share my experience so far. I will do this in the form of an interview using some questions I have gotten over time.
Presented a hands-on workshop at Build with AI 2025, demonstrating how to build a practical movie recommendation system from scratch. Covered collaborative filtering, content-based approaches, and real-world implementation strategies for Google Developer Groups audience.
Whether you're looking to collaborate on ML projects, discuss opportunities, or chat about the latest in AI, I'd love to hear from you.