CURRICULUM VITAE · PRIMARY IDENTITY
Oluwajuwon Adediji · Business analytics, quantitative analysis, financial modelling, BI/SQL, economics, and AI evaluation.
Use this identity across applications, portfolio headers, CV presentation, and professional profiles.
AI is a workflow multiplier and quality-control specialization within the broader analytical profile.
I turn ambiguous business, financial, and economic questions into validated analytical outputs. My work emphasizes decision framing, data quality, quantitative reasoning, transparent assumptions, reproducibility, and decision-oriented communication.
Core value: connect the analytical method to the decision the evidence is meant to support.
Business analytics: KPI diagnostics, revenue and margin analysis, segmentation, root-cause analysis, dashboard narratives.
Quantitative analytics: statistical modelling, risk segmentation, scenario analysis, sensitivity analysis, hypothesis testing.
Financial analytics: forecasting, unit economics, break-even, ROI, planning models, model QA.
BI / data: SQL analytical layers, Python/pandas, data validation, reconciliation, KPI design, Power BI modelling concepts and dashboard design.
AI evaluation: claim-level evaluation, evidence checks, completeness, traceability, consistency, cost/latency analysis, failure taxonomy.
Reproducible retail case from data-quality audit through H1/H2 decomposition, price-volume analysis, segmentation, anomaly triage, executive diagnosis, and static BI evidence.
Open case study ↗SQL-backed KPI views, management comparison logic, dimensional reporting, validation checks, and a Power BI build specification extending the flagship case.
Open case study ↗Logistic probability-of-default modelling and dynamic FICO bucketing with seeded data, evaluation, monotonicity checks, and unit tests.
Assumption-driven forecasting, scenario management, sensitivity analysis, and model quality assurance.
Contribution, break-even, ROI, and sensitivity modelling for pricing and commercial trade-offs.
Repeatable measurement of accuracy, completeness, traceability, consistency, cost, latency, and failure patterns in AI-assisted research workflows.
Applied analysis using official World Bank indicators, with explicit source provenance, indicator periods, and measurement limitations.
Open case study ↗Forage job simulations: JPMorgan Chase and Deloitte analytical/business simulations, clearly presented as simulations rather than conventional employment.
Applied public-data work: Nigeria Development Data Monitor using official World Bank indicators to demonstrate evidence-backed analysis outside synthetic datasets.