Data Analyst & Operations Professional
I turn complex data into clear decisions. With 3+ years spanning analytics, operations management, and customer-facing roles, I bring both the technical depth and the operational instinct that most analysts don't.
Building dashboards, predictive models, and analytical pipelines that help teams make smarter, faster decisions. From exploratory analysis to ML models — I make data actionable.
Managing processes, tracking KPIs, and building reporting systems that keep operations running efficiently. I've managed teams, projects, and documentation across multiple industries.
Combining genuine customer empathy with data-driven thinking. I analyse customer behaviour, resolve escalations, and use insights to improve service quality and retention outcomes.
End-to-end fraud detection system built with a trained Random Forest model (saved as .pkl). Includes a live Streamlit web app where users can input transaction data and get real-time fraud predictions. Full pipeline: data preprocessing, model training, deployment.
View on GitHub →Interactive Streamlit dashboard analysing real estate customer leads — built on top of a full exploratory data analysis (EDA) notebook. Surfaces customer behaviour patterns, lead quality signals, and conversion insights from a live CSV dataset.
View on GitHub →Geocoded and cleaned electoral data for Delta State polling units. Built an interactive HTML map visualising unit locations, applied outlier detection algorithms, and generated a structured anomaly report — combining geospatial analysis with data quality validation.
View on GitHub →Analysed 22 years of Lagos weather data to identify flood risk patterns, seasonal trends, and high-risk periods. Combined historical climate records with geospatial context to surface actionable insights for urban risk planning.
View on GitHub →Built and compared three classification models (Decision Tree, Logistic Regression, Random Forest) to predict loan eligibility. Includes feature engineering, preprocessing, model persistence (.joblib), and performance evaluation across all three approaches.
View on GitHub →Pulled live data via the FRED API to analyse correlations between M1 money supply, US inflation rates, and USD/EUR exchange rates. Produced clear visualisations mapping macroeconomic relationships across time — merging multiple financial datasets into one analytical pipeline.
View on GitHub →Interactive analytics built directly on this page — demonstrating real data skills you can see without clicking away.
Available for remote roles in Data Analysis, Operations Analytics, and Customer Success. Open to international opportunities.
tinauyats@gmail.com