Professional Profile
Building reliable systems from data, models, statistical reasoning, and real-world requirements.
I am building a career at the intersection of data science, machine learning, AI engineering, and applied research.
My experience includes machine learning workflows, statistical modeling, Retrieval-Augmented Generation systems, data engineering and validation, business intelligence, geospatial analytics, forecasting, cloud AI development, and research computing.
Across my work, I focus on three things: understanding the problem clearly, building a defensible technical solution, and evaluating whether that solution works reliably in practice.
Applied Machine Learning
Predictive modeling, anomaly detection, forecasting, model comparison, explainability, and performance evaluation.
Research-Driven AI
Statistical learning, concentration inequalities, dependable AI, reproducible experimentation, and rigorous validation.
Data and Decision Systems
Data quality, ETL, business intelligence, algorithmic workflows, cloud systems, and decision-support applications.
Career Journey
From reliable data systems to evaluation-driven applied AI.
My career has developed through a connected sequence of data systems, analytics, machine learning, statistical research, and AI engineering.
Data Systems
I began by working with databases, structured and geospatial data, secure access, query performance, reporting pipelines, and data integrity.
Analytics and Business Intelligence
My work expanded into ETL automation, institutional analytics, validation workflows, dashboard development, and stakeholder reporting.
Machine Learning
I moved into predictive modeling, forecasting, classification, anomaly detection, model comparison, explainability, and business impact.
Statistical Research
My cybersecurity research connects probability theory, concentration inequalities, statistical learning, and certified anomaly detection.
Applied AI Engineering
Project Ariel brings my interests together through knowledge-base engineering, embedding retrieval, grounded generation, evaluation workflows, feedback analysis, and cloud deployment.
Professional Experience
About three years of focused experience across analytics, data systems, machine learning, and applied AI-related work.
Center for Research Libraries
Algorithm & Data Engineer Intern
Worked on workflow optimization and allocation-system design across 19 research institutions. I prepared and validated institutional data, supported structured matching and allocation logic, evaluated assignment behavior, developed diagnostics, and translated operational requirements into implementation-ready analytical workflows.
Related work: Reciprocal Resource MatchingCanisius University
Graduate Assistant, Data Analytics & Reporting
Supported institutional analytics, enrollment reporting, ETL automation, data validation, and dashboard development. My work included SQL, SAS PROC SQL, R, Alteryx, SLATE CRM, MS SQL Server, Tableau, and Power BI, along with documented data-quality and reporting workflows.
Related work: Visual Analytics & BIGeospatial Intelligence and Data Analysis Centre, Ministry of Defense
Pioneer Database Administrator & Geospatial Data Analyst
Supported database administration, secure data access, SQL optimization, data integrity, geospatial dataset preparation, and Python-assisted reporting workflows. This role established my foundation in reliable data systems and operational analytics.
Education
Graduate analytics training supported by an economics foundation.
Graduate Education
Master of Science, Data Analytics (Big Data)
Canisius University, Buffalo, New York
Completed December 2025
My graduate education strengthened my foundation in statistical modeling, machine learning, predictive analytics, forecasting, data preparation, visualization, business intelligence, and applied research workflows.
Undergraduate Education
Bachelor of Science, Economics
University of Abuja, Nigeria
My economics background developed my ability to think about systems, incentives, resource allocation, decision-making, and measurable business impact. That perspective continues to shape how I frame machine learning and analytics problems.
What I Work On
Technical areas where analytical depth and practical system-building meet.
Applied Machine Learning
Predictive and anomaly-detection workflows, model comparison, evaluation, explainability, and decision support.
Retrieval-Augmented Generation
Knowledge-base design, embedding retrieval, grounded generation, evaluation suites, feedback loops, and maintainable AI assistants.
Cybersecurity Analytics
Statistical learning, probability theory, dependable AI, and certified anomaly detection for IoT and network data.
Energy Analytics
Forecasting, explainability, residual analysis, optimization, and operational recommendations for energy systems.
Evaluation-Driven AI
Retrieval audits, smoke tests, baselines, validation pipelines, feedback analysis, and structured quality improvement.
Current Direction
Pursuing roles that combine model development, software thinking, statistical reasoning, and real-world problem solving.
I am particularly interested in applied machine learning, RAG systems, cybersecurity analytics, energy analytics, statistical anomaly detection, cloud AI development, and evaluation-driven AI systems.
Contact
Let’s connect.
I am open to data science, machine learning, applied AI, and research-oriented opportunities where strong analytical thinking and reliable system development matter.
Email and LinkedIn are the best ways to contact me for professional opportunities, collaborations, or technical discussions.