Data × AI × Software.
I build systems that prove themselves — every number traces to a source, every decision has a record.
I'm a software engineer and data scientist with five years of experience building production systems in fintech, payments, and logistics. My work sits at the intersection of three disciplines:
→ Software Engineering — backend services, APIs, distributed systems → Data Science — analytics, statistical modeling, feature engineering → Applied AI — LLM integration, fraud detection, explainability layers
EDUCATION
PhD in Data Science — In Progress
M.Sc. in Data Science — Northeastern University
M.Sc. in Information Technology — University of the People
B.Sc. in Accounting — Osun State University
That progression was deliberate. Accounting taught me that every number must have a source and a counterparty. Data Science taught me how to find patterns in those numbers. Engineering taught me how to turn those patterns into systems that run in production.
WHAT I BUILD
Payments infrastructure — idempotency, state machines, HMAC-verified webhooks, immutable audit trails.
Reconciliation systems — O(n) matching engines, five classification statuses, value-at-risk reporting.
AI systems — traceability-constrained LLM layers, fraud detection models with explainable scores.
Five projects on GitHub. Two live. All tested.
SELECTED WORK
→ Payments API (live): payment-api-kwe9.onrender.com → Reconciliation Engine: github.com/Daniel38215571/reconciliation-engine → Reconciliation LLM Layer: github.com/Daniel38215571/reconciliation-llm-layer
WHAT I CARE ABOUT
The interesting engineering is not in the happy path. It is in the failure modes — the concurrent request, the invalid state transition, the empty input, the forged webhook. Systems that prove themselves are systems that handle what goes wrong.
Traceability before intelligence. Explain a number you can already prove, not one you're hoping is right.
Open to remote-first engineering and data science roles in fintech. Available immediately.
Lagos, Nigeria (WAT/UTC+1). M.Sc. Data Science (Northeastern) · M.Sc. Information Technology · B.Sc. Accounting. 5+ years building fintech and logistics systems. I turn messy operational data into auditable pipelines, tran
Traceable data first. Intelligence second.
| Project | What it does | Stack |
|---|---|---|
| forensic-healthcare-billing-screen | Peer-group anomaly screening for healthcare billing. Hierarchical peer fallback, services-weighted benchmarks, robust z-scores, composite screening score. 18 passing tests. | Python · pandas · Excel |
| Executive-Sales-Dashboard | Executive sales dashboard with KPI traceability and pivot-table evidence. | Excel · pivot analysis |
| amazon-financial-sql-analytics (in progress) | SQL analytical model for Amazon financials with executive dashboard. | SQLite · SQL · Excel |
Notebook-based exploratory and modeling work — migration from Colab in progress.
Fraud detection project in development.
| Project | What it does | Stack |
|---|---|---|
| reconciliation-engine | O(n) transaction reconciliation engine for fintech. Hash-map matching, five-status classification, value-at-risk reporting, FastAPI service. 10 passing tests. | Python · FastAPI · pytest |
- 🔍 LLM explanation layer on top of the reconciliation engine — traceable data first, intelligence second
- 📊 Migrating analytics projects to SQL
- 📓 Migrating data science notebooks to GitHub
