Technical Expertise
Programming & ML
Python (pandas, sklearn, TensorFlow), SQL, R, GitLab, NumPy, Matplotlib
Machine Learning
Classification, model optimisation, SMOTE, feature engineering, CNN/LSTM, hyperparameter tuning
Data Engineering
ETL pipelines, Azure (Synapse/Storage), data modelling, large-scale dataset processing
Analytics & Visualisation
Power BI, DAX, Excel advanced formulas, pivot tables, dashboards
Employment History
EDMS Support Analyst
Warri Refinery & Petrochemical Company
• Supported digital document management across departments. • Assisted engineers with data retrieval and technical documentation. • Performed structured data classification, indexing, and archiving. • Ensured data integrity and accessibility for stakeholders.
Data Clerk
Hequip Resources
• Managed daily data processing, documentation, and quality assurance tasks. • Developed structured data collection systems improving reporting accuracy. • Cleaned, validated and updated sensitive records ensuring compliance and accuracy. • Supported management with ad-hoc analysis and produced reports on operational trends.
Data Analyst
PICKMEUP.NG
• Analysed rich operational datasets to support decision-making and customer experience improvements. • Merged, cleaned and processed datasets using SQL, Python, and Excel. • Designed and delivered analytical dashboards using Power BI. • Identified business process inefficiencies and proposed data-driven improvements. • Collaborated cross-functionally with engineering and product teams to translate user and operational needs into actionable insights.
Key Achievements
- Built predictive ML models with strong classification accuracy, validated through statistical tests.
- Processed datasets exceeding 65 million rows using Spark, reducing computation times significantly.
- Automated reporting workflows that reduced processing time by up to 40%.
- Created Power BI dashboards used by multiple stakeholders to support data-driven decision-making.
- Demonstrated resilience, emotional intelligence, and calm communication in high-pressure environments.
Research Interests
- Medical image analysis
- Predictive modelling
- Hybrid ML systems (deep learning + classical ML)
- Bayesian optimisation
- Health informatics
- Efficient/Green AI architectures
Selected Projects
Highlights from my recent work
Tinnitus Detection Using Machine Learning
Developed a predictive model to classify tinnitus presence using extracted features from patient data. Built and evaluated multiple ML algorithms (SVM, Random Forest, Logistic Regression), implemented SMOTE, and achieved strong performance.
View ProjectECG Heartbeat Classification
Applied ML techniques to classify arrhythmia patterns on medical data. Developed automated preprocessing pipeline for signal segmentation and tested models including CNN-based approaches.
View ProjectViolent Crime Big Data Analysis
Processed and analysed multi-million-row datasets (65M+ Rows) using PySpark and Azure Databricks. Built automated ETL pipeline and delivered insight dashboards for regional crime trends.
View ProjectLatest Thoughts
Insights on data, tech, and building
The Future of AI in Healthcare
How machine learning models are revolutionizing diagnostics and patient care....
Read Article →Scaling Data Pipelines for Big Data
Best practices for processing millions of rows efficiently using Apache Spark....
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