I work at the intersection of data, mathematics, and intelligent systems — spanning roles as a Data Scientist, AI Engineer, Machine Learning Practitioner, and Software Engineer. With a foundation in Mathematics and Computer Science, I build models, pipelines, and production-ready software that turn raw data into real insight. I'm also passionate about education, and enjoy breaking down complex ideas to make them accessible to others.
Analyzing and interpreting complex datasets to extract meaningful insights. I work with tools like Python, pandas, NumPy, and scikit-learn, applying statistical methods and machine learning to solve real problems — from data cleaning and EDA to hypothesis testing and predictive modeling.
Training and evaluating models that learn from data. My work spans classification, clustering, and trend analysis — including ML pipelines for genre classification, mood detection, and language identification as part of larger data-driven platforms.
Building intelligent, production-ready AI systems end to end. I work with large language models, RAG pipelines, vector databases like Pinecone, and frameworks like LangChain — designing systems that go beyond experimentation into real, deployable solutions.
Writing clean, maintainable code across the stack. I build web applications using HTML, CSS, JavaScript, and frameworks like Next.js, with backend experience in Python and PHP — delivering polished, functional software for real clients and personal projects.
Everything I build is grounded in a strong foundation in Mathematics and Computer Science. From probability and statistics to algorithms and computational thinking, this background shapes how I approach and solve complex problems.
Passionate about making technical knowledge accessible. Whether simplifying AI engineering concepts, breaking down statistics, or sharing what I learn through talks and community events like Build with AI Nairobi, I believe in the power of teaching to multiply impact.