About
Data Scientist with a unique blend of customer success and advanced analytical expertise. I specialize in using SQL, Python, and Power BI to transform complex datasets into clear, actionable insights. With 7+ end-to-end projects and training from Utiva, I leverage Generative AI to build predictive models and interactive dashboards. I am passionate about bridging the gap between technical data and user-centric design to drive business growth.
Work
Technical Data Apprentice (AI-Led)
Remote
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Summary
Executed 7+ full-stack data projects—including a 50,000-row NLP analysis—by integrating SQL, Python, and Power BI within an AI-led technical apprenticeship.
Highlights
Executed 7+ data projects, managing the full lifecycle from raw data extraction in SQL to building advanced, interactive dashboards in Power BI.
Engineered a sentiment analysis pipeline for 50,000 IMDB reviews using Python for text cleaning (NLP) and Machine Learning to classify customer "vibes."
Leveraged Generative AI to debug complex code and optimize SQL queries, increasing development speed by 40% while maintaining high data accuracy.
Moniepoint
|Customer Success - Messaging
Lagos, Nigeria
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Summary
Engineered and deployed analytical frameworks on messaging datasets to optimize customer response times and boost engagement efficiency, driving data-driven product and service enhancements.
Highlights
Performed customer churn analysis and trend forecasting on support data, providing data-driven insights that directly enhanced resolution efficiency and reduced ticket volume.
Conducted deep-dive analysis of comprehensive messaging data to segment customer concerns and identify recurring issues, leading to targeted product and service enhancements.
Designed and implemented KPIs (Key Performance Indicators) to measure customer satisfaction and evaluate chatbot performance, providing data to optimize automated support systems.
Bankly MFB
|Product Support
Lagos, Nigeria
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Summary
Developed automated reporting systems to enhance visibility into customer concerns and resolution metrics, significantly improving operational oversight.
Highlights
Utilized SQL and Power BI to manage and analyze extensive customer support datasets, proactively identifying recurring product issues and informing targeted improvements in product usability.
Streamlined issue resolution workflows by conducting user data analysis, resulting in a quantifiable increase in support efficiency and faster customer issue resolution.
Tracked and logged customer issues, ensuring swift resolutions and identifying systemic patterns to proactively address larger product problems.
Freelance
|Product Designer
Lagos, Nigeria
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Summary
Integrated data-driven usability testing and user-centered design principles to lead product initiatives, resulting in a documented 20% boost in customer satisfaction and enhanced product functionality.
Givanas Nig. Ltd.
|Logistics Officer
Lagos, Nigeria
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Summary
Implemented data-informed logistics strategies and defined operational policies, resulting in optimized service delivery and a formalized end-to-end workflow.
Ubereness Nig. Ltd.
|Account Payable Officer
Ibadan, Nigeria
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Summary
Managed full-cycle accounts payable, resolving complex billing discrepancies to achieve a 20% reduction in payment errors and upholding stringent financial data integrity.
Projects
HR Analytics and Employee Performance Dashboard
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Summary
This project focuses on identifying the root causes of employee attrition at Indicino and building a predictive model to flag employees at high risk of leaving. The goal is to provide the HR Group Head with actionable, data-backed recommendations to reduce the overall turnover rate of 16.12%. Tools Used: Pandas, Numpy, Matplotlib, Seaborn, and Scikit-Learn.
Customer Churn Prediction Model
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Summary
Goal: Companies always want to know who is about to leave (churn). This project proves the use of Machine Learning to save money by identifying at-risk customers. - Developed a predictive classification model (e.g., Random Forest Classifier) using Python (Scikit-learn and Pandas) to forecast customer churn. - Cleaned, engineered, and transformed a proprietary dataset of 50,000 customer records, achieving an optimal feature set for model training. - The final model achieved an accuracy of 86%, providing marketing teams with a prioritized list of high-risk users for targeted retention campaigns. - Tools Used: Python, Scikit-learn, Pandas, Matplotlib, Hypothesis Testing.
Education
UTIVA
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Bootcamp
Data Science
Tai Solarin University of Education
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BSc.
Economics
Skills
Programming Languages
Python, Pandas, NumPy, Matplotlib, Scikit-learn, SQL.
Data Analysis & Visualization
Power BI, Data Visualization, Data Analysis.
Machine Learning
Supervised Learning, Unsupervised Learning, Model Evaluation.
Database Management
PostgreSQL.
Statistical Analysis
A/B Testing, Hypothesis Testing.
Business Intelligence
Customer Segmentation, Churn Prediction.
Soft Skills
Problem-Solving, Communication, Team Collaboration.