🎓 Data Scientist | Machine Learning Specialist (MLOps Tools & Cloud Practices)
📊 M.Sc. in Information Science (Machine Learning Focus) — University of Arizona, 2025
🏆 Certified in MLOps (DataTalks.Club Zoomcamp, 2025) | Oracle AI Foundations Associate (2025)
💡 From verifying data to deploying it — I make sure every model I build earns its integrity.
I’m a Data Scientist who builds machine learning systems that can be trusted — not just to predict, but to perform.
My path has been deliberate:
- Business Insight: At Northland Properties, I learned how data reveals operational and customer stories that drive performance.
- Data Integrity: At the Canada Revenue Agency, I applied analytical discipline and automation to ensure payroll and financial data were accurate, traceable, and compliant.
- Data Intelligence: At Manifest & Company Inc., I now design and deploy end-to-end ML pipelines using MLflow, Airflow, Docker, Terraform, and GCP — building scalable, auditable, cloud-native models.
I’m passionate about creating reliable automation — ML workflows that are reproducible, explainable, and measurable long after deployment.
My goal isn’t just accuracy — it’s impact.
Models that don’t just predict, but prove their integrity.
Business Insight → Junior Business Analyst, Northland Properties (2017–2021) — turned raw sales data into insights through KPI dashboards and analytics.
Data Integrity → Trust Accounts Examination Officer, Canada Revenue Agency (2021–2024) — automated compliance workflows and strengthened large-scale financial data integrity.
Data Intelligence → Data Scientist (MLOps Projects), Manifest & Company Inc. (2025–Present) — designed, deployed, and monitored cloud-native ML pipelines with MLflow, Airflow, Docker, and GCP.
✨ I believe that data science is only valuable when it drives real-world change — turning insight into action, and models into measurable impact.
Below are a few of my highlighted projects demonstrating practical applications of data science, deep learning, and MLOps principles.
Production-grade ML pipeline predicting loan defaults using the LendingClub dataset.
- Automated training, tuning, deployment, and monitoring using Airflow, MLflow, and Evidently AI
- Deployed with Terraform + GCP and continuous integration via GitHub Actions
Supervised learning pipeline for credit risk prediction using structured financial data.
- Trained and compared Random Forest, XGBoost, and SVM models with feature engineering
- Addressed class imbalance via oversampling; achieved ~90% accuracy and 96% recall
Real-time computer vision system for autonomous vehicle perception.
- Fine-tuned YOLOv5 on the OpenImages dataset (5 vehicle classes)
- Achieved mAP@0.5 ≈ 0.65, balancing detection speed and accuracy
CNN-based Siamese network for face verification using triplet loss.
- Built an embedding architecture in TensorFlow/Keras
- Achieved 98% verification accuracy across multi-class face recognition tasks
Fine-tuned Transformer models for commonsense reasoning and explanation generation.
- Used Hugging Face Transformers and SemEval 2020 ComVE dataset
- Achieved BLEU ≈ 0.22, ROUGE ≈ 0.46
Data visualization dashboards showcasing sales performance and vendor KPIs.
- Designed interactive dashboards with Matplotlib and Pandas
- Delivered actionable insights for inventory and sales optimization
- Machine Learning & Predictive Modeling
- MLOps & Model Deployment Pipelines
- Feature Engineering & Data Preprocessing
- Neural Networks & Deep Learning (CNNs, RNNs, Transformers)
- Cloud & Infrastructure Automation (GCP, Terraform, Docker)
- Exploratory Data Analysis (EDA) & Business Intelligence
- Risk, Compliance & Financial Analytics
- Data Integrity & Process Automation
🎓 M.Sc. in Information Science (Machine Learning Focus) — University of Arizona (Jan 2023 – May 2025)
📜 Official Degree Verification
🎓 Post-Baccalaureate Diploma in Human Resources Management — Thompson Rivers University (2014 – 2016)
🎓 B.Sc. in Zoology — University of Ibadan (2010)
🏆 MLOps Zoomcamp — DataTalks.Club (Apr – Aug 2025)
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🏆 Oracle Certified Foundations Associate — AI Foundations (Oct 2025) — Oracle Cloud Infrastructure (OCI)
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💡 Currently exploring: LLM-based workflow orchestration, real-time model monitoring, and generative AI integration in MLOps pipelines.
I’m always open to connecting with professionals in data science, machine learning, and cloud engineering — to collaborate, share knowledge, or build something that drives measurable business value.
✨ Thanks for visiting my profile! Feel free to explore my repositories — every project here reflects my belief that machine learning should be transparent, reliable, and impactful.