I turn messy, complex data into stories that drive real decisions. SQL, Python, Power BI — I've used all three to build dashboards, detect fraud, and predict churn for financial and fintech datasets.
I'm Mary — a data analyst based in Akure, Nigeria, with 4+ years of hands-on analytical practice across financial data, transaction monitoring, fraud detection, and predictive modeling.
My work lives at the intersection of finance and data. I've built bank transaction dashboards that simulate executive-level KPI reporting, written SQL scripts that flag fraudulent credit card activity, and trained machine learning models that predict customer churn with measurable accuracy.
I'm driven by a genuine curiosity about how money moves — how patterns in transaction data reveal risk, opportunity, and behavior. Every project I build teaches me something new about that.
Currently seeking fully remote data analyst roles where I can contribute real analytical value from day one.
Four projects. Real datasets. Every one built end-to-end — from raw data to actionable insight.
Designed a relational database schema to store and classify bank transactions by channel, type, and customer segment. Wrote SQL queries to compute revenue by channel, transaction volume, average ticket size, and month-over-month growth. Built an interactive Power BI dashboard with KPI cards and trend charts — simulating executive-level performance reporting for a retail bank.
Cleaned and preprocessed a raw fintech dataset in Python — resolving duplicates, nulls, and format inconsistencies. Performed exploratory data analysis to surface behavioral patterns across customer segments and time periods. Created visualizations including bar charts, heatmaps, and time-series plots to communicate insights clearly to non-technical audiences.
Wrote SQL scripts to investigate large credit card transaction datasets and flag unusual patterns — including velocity checks, geo-anomalies, and statistical outliers. Structured queries for automated pattern filtering across large datasets. Summarized high-risk cases into a compliance-ready report bridging raw query output and actionable security intelligence.
Built classification models — Logistic Regression and Decision Tree — to predict customer churn probability from behavioral data. Evaluated model performance using accuracy, precision, recall, and F1-score. Went beyond the model: interpreted feature importance and translated findings into actionable business recommendations for retention strategy.
Not buzzwords — the actual tools and thinking behind every project.
Writing optimized queries to extract insights from complex, large-scale transaction datasets.
End-to-end data workflows — cleaning, analysis, visualization, and predictive modeling.
Turning raw data into executive-ready dashboards that non-technical stakeholders actually use.
Pattern detection, anomaly identification, and structured reporting in financial data contexts.
Building, evaluating, and interpreting classification models for real business outcomes.
Clear, structured analytical reports and data stories built for both technical and non-technical audiences.
Open to fully remote data analyst roles, freelance analytical projects, and any opportunity where clean data thinking makes a real difference. I respond promptly and love a good brief.
Available immediately for fully remote positions. Comfortable overlapping with EST, CET, or WAT timezones. Fast communicator, self-directed, and deadline-driven.
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