Qudirah Alimi

I am a Machine Learning Engineer who builds and deploys models that solve real problems across finance, agriculture, climate, and consumer products. My work sits at the point where research meets application—designing systems that are accurate, scalable, and usable.

I have developed a recommendation engine that processes over 70,000 opportunities, enabling organizations to match more effectively with funding. In financial services, I built a credit scoring model with an 81% recall rate, improving the prediction of loan defaults and supporting access to credit. For agriculture, I created a regression model with a 92% R², helping farmers estimate crop loss ratios with far greater accuracy. In climate resilience, I designed a model to predict population displacement with over 70% accuracy, giving communities better tools for disaster preparedness.

I also create AI-driven products that people can interact with directly. One example is a skincare consultant powered by OCR and NLP, which identifies harmful ingredients and provides tailored advice. Projects like this reflect my interest in making machine learning not only functional but also approachable and engaging.

Beyond building systems, I invest in education and communication. I have trained students in Python, SQL, and machine learning, raising their proficiency levels significantly. I design dashboards and reports that make complex results easy to understand and act upon, bridging the gap between technical models and decision-making.

I contribute to open-source software as well, improving documentation and test coverage in libraries used worldwide. These contributions keep me connected to a global community of developers and researchers while sharpening my own practice.

The thread running through my work is impact. Whether in predicting financial risk, improving food security, planning for climate change, or creating consumer-facing AI, I focus on outcomes that matter. I approach problems with the discipline of a builder, measure results, refine, and deliver, while staying curious about where machine learning can go next.

I am looking for a remote role where I can deepen my expertise, work with diverse teams, and apply machine learning to challenges that require both rigor and creativity.