Data Scientist · AI Trainer / Data Annotator · Web3 / Smart Contracts · Kaduna, Nigeria
Self-taught, detail-driven Data Scientist with a BSc Statistics foundation and hands-on experience across three tracks: data science, AI training & data annotation, and Web3/blockchain engineering. Proven ability to translate raw datasets into decisions and communicate findings clearly to non-technical audiences.
Execute Reinforcement Learning from Human Feedback (RLHF) tasks to improve the reasoning, accuracy, and safety of large language models Evaluate search engine results for intent and accuracy, applying structured rubrics to flag mismatches and low-quality outputs Annotate large-scale text, image, and audio datasets under strict deadlines, maintaining Gold Standard accuracy standards
Managed digital documentation and data entry projects, ensuring high data integrity and organization Self-regulated daily workflows to consistently hit productivity targets without compromising quality control Troubleshot technical issues in proprietary software tools to maintain project momentum
Built a predictive framework to forecast Premier League trajectories and performance metrics for sports investors and business stakeholders Combined machine learning modeling with Power BI dashboards to translate predictions into business decisions Developed in collaboration with CoLab Innovation Hub, Kaduna
Technologies: Python, scikit-learn, Power BI
Analyzed sleep patterns across 62 mammal species using the Allison & Cicchetti (1976) dataset Key findings: larger animals sleep significantly less; animals in dangerous environments sleep ~65% less; the Danger Index emerged as the strongest predictor of sleep behavior Published a public write-up of findings on Medium
Technologies: Python, Pandas, Matplotlib, Seaborn
Published a widely-read article on practical trend detection (moving averages, linear regression with p < 0.05, seasonal decomposition via Python) and real-world ML applications across healthcare, logistics, agriculture, and public safety Positioned statistics and explainable ML as the foundation for junior data scientists — emphasizing the 'So What?' mindset over model complexity Additional case studies in progress — portfolio updated on an ongoing basis as new analyses are completed
Contributed to a modular Soroban smart-contract toolkit used for token minting on the Stellar blockchain Worked within a fork → branch → fix → test → PR → CI workflow
Technologies: Soroban smart contracts, TypeScript
Resolved backend issues on a Stellar payment-gateway API via forked contributions Followed a one-PR-per-issue workflow with CI validation before merge
Fixed open issues and improved reference examples Ensured all dependencies installed cleanly and CI checks passed before each PR