I build the intelligence layer between raw data and executive decisions.
Seven years. Four industries. Three continents. From public health systems in Nigeria to fintech ops in the US, I architect AI-powered analytics that don't just report what happened but shape what happens next.
Most data professionals hand you a dashboard and call it done. I build the systems behind the systems.
Multi-agent AI pipelines, product intelligence frameworks, and operational analytics architectures that give leadership real clarity, not just real-time numbers.
With a B.Sc. in Physics from FUTA and an active MSc in Data Science, I bring rigorous analytical thinking to every layer of the stack, from ETL pipelines and SQL models to LLM prompt architecture and cross-functional performance strategy.
I've embedded intelligence into public health operations in Nigeria, led product analytics at scale in US fintech, built branded analytics programmes in the UK, and shipped open-source AI systems that benchmarked to production standards.
Currently operating at the intersection of product, data, and AI and open to senior roles across the US and EMEA.
Built a production-grade multi-agent AI system to coordinate national emergency dispatch across 8 Nigerian cities, handling 89 concurrent incidents, 3 operational shifts, and real-time cross-city resource conflict detection.
The architecture deliberately assigns specialised agents to distinct tasks rather than overloading a single model, proving that role-based agent orchestration outperforms monolithic LLM approaches on complex, time-critical operational problems at scale. Fully open-sourced for the AI and emergency management community.
Each agent handles a distinct operational role: dispatch coordination, resource allocation, conflict detection, and shift handover, with no single model overloaded. Deployed and validated in a containerised environment against a structured multi-city incident corpus.
Built a production-grade fraud detection and risk scoring system aligned with CBN, Visa, and Mastercard compliance frameworks. Combines an ensemble ML layer (XGBoost, Random Forest, Logistic Regression) with a deterministic rules engine and a 3-zone blend scoring model calibrated against four real-world fraud datasets.
Deployed as an interactive Streamlit application with single-transaction scoring, bulk upload analysis, KRI dashboards, and a Monte Carlo stress-testing module with configurable rule slip rates and VaR output.
Ensemble weights calibrated across PaySim, Credit Card, BAF, and Nigerian fintech data. Rules engine and ML scores blend across three risk zones (GREEN, AMBER, RED) with Monte Carlo VaR stress testing at configurable slip rates.