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Senior Analyst with 7+ years in Healthcare Data Analytics & AI
Data Analyst and Applied AI Professional with 5+ years of experience in healthcare analytics, predictive modeling, business intelligence, and operational data systems across healthcare, banking, and enterprise environments. Experienced in developing data-driven solutions using SQL, Python, machine learning, statistical analysis, and scalable reporting systems to support operational efficiency and strategic decision-making. Skilled in healthcare analytics, ETL pipelines, predictive forecasting, dashboard development, and workflow automation, with experience supporting healthcare systems, public-health analytics, and enterprise data initiatives. Strong collaborator with the ability to bridge technical and business stakeholders in complex data environments.
Indiana University of Pennsylvania
Master of Science · Applied Mathematics (Data Science Specialization)
N/A – June 30, 2024
Crawford University
Bachelor of Science · Computer Science & Technology
N/A – June 30, 2016
Detapent Inc.
Data Analyst
June 1, 2025 – Present
Texas, USA
Illinois Department of Healthcare & Family Services
Information Systems Analyst
September 1, 2024 – June 1, 2025
Illinois, USA
Indiana University of Pennsylvania
IT Service Customer Support
May 1, 2023 – August 1, 2023
Pennsylvania, USA
Polaris Bank Limited
Business Process & Data Analyst
September 1, 2021 – July 1, 2022
Lagos, Nigeria
Coronation Merchant Bank
Reconciliation Data Analyst
September 1, 2017 – August 1, 2021
Lagos, Nigeria
Customer Orders ETL Pipeline
June 21, 2026 – Present
Designed an ETL pipeline using Python, Pandas, SQLAlchemy, and MySQL to clean, transform, and load transactional data.
Customer Review Sentiment Analyzer
June 21, 2026 – Present
Built an interactive R Shiny application to analyze customer reviews and visualize sentiment trends using NLP techniques.
Bayesian A/B Testing on Customer Churn
June 21, 2026 – Present
Implemented a Bayesian A/B testing framework to estimate churn reduction and probability of improvement.
Enhanced BSTS and LSTM Forecasting Model
June 21, 2026 – Present
Developed an ensemble forecasting framework combining Bayesian Structural Time Series (BSTS) and Long Short-Term Memory (LSTM) models to improve predictive performance for irregular and mixed-frequency healthcare time-series data derived from wastewater epidemiology datasets.
Google Business Intelligence Certificate
June 1, 2026 – Present
Six Sigma Yellow Belt
Unknown
June 1, 2026 – Present
Google Data Analytics Professional Certificate
June 1, 2026 – Present
Google Advanced Data Analytics Certificate
June 1, 2026 – Present
Professional Scrum Master (PSM I)
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from healthcare forecasting to customer sentiment analysis and ETL pipelines, indicates a broad interest and adaptability. Their experience across different industries (healthcare, banking, enterprise) and roles (Data Analyst, Information Systems Analyst, Business Process & Data Analyst) suggests a versatile professional who can adapt to various organizational cultures and challenges. The focus on process improvement and data-driven decision-making aligns well with a results-oriented culture. However, the projects are primarily personal, and the professional experience, while relevant, is relatively short-term in some instances, which might require further probing on long-term commitment and team integration.
Soft Skills & Operational Fit
The candidate demonstrates strong analytical and problem-solving skills, evidenced by their project work and professional achievements in improving data pipeline efficiency and reducing manual reporting processes. Their experience in collaborating with technical and business stakeholders, gathering requirements, and communicating analytical findings suggests good communication and teamwork abilities. The certifications in Scrum Master and Six Sigma Yellow Belt indicate an understanding of agile methodologies and process improvement frameworks, which are valuable for operational fit.