📧 Email • 💻 GitHub • 🔗 LinkedIn
I’m a Data Scientist & Machine Learning Engineer who builds reproducible, scalable, and cloud-ready ML systems. My focus is on:
- End-to-end ML pipelines (training → evaluation → deployment → monitoring)
- Cloud ML deployment using Vertex AI & Cloud Run
- Experiment tracking & registries using MLflow
- Containerized training + inference with Docker
- Automated workflows using Airflow
- Data validation & drift detection using Evidently AI
Across my roles, my work has consistently centered on clean data, reliable pipelines, and production-grade ML systems.
Design, deploy, and maintain cloud-based ML systems using Vertex AI, Cloud Run, MLflow, Docker, and Airflow.
- Built reproducible training & evaluation pipelines on Vertex AI
- Containerized preprocessing, training, and inference workflows
- Implemented experiment tracking + model registry with MLflow
- Automated batch inference, retraining & feature workflows with Airflow
- Added monitoring & drift detection workflows using Evidently AI
- Integrated CI/CD workflows using GitHub Actions
- Created validation logic reducing manual review by ~25%
- Reconciled multi-system financial datasets
- Built anomaly detection pipelines for compliance classification
- Standardized audit-ready reconciliation workflows
- Automated SQL/Excel workflows (40% reduction in manual reporting)
- Consolidated cross-property data for analytics pipelines
- Built KPI dashboards & standardized data definitions
Tech: XGBoost, MLflow, Docker, Airflow, Vertex AI, Evidently AI, Optuna
- Full MLOps pipeline: preparation → training → evaluation → deployment → monitoring
- Deployed on Cloud Run with MLflow registry integration
- Automated batch inference & drift detection in Airflow
Tech: SentenceTransformers, FAISS, FastAPI, Streamlit, Phi-3 Mini
- Modular ingestion → embedding → retrieval → generation pipeline
- FAISS-optimized retrieval with evidence tracing
- Streamlit UI with reliability features
- Classification + generative explanation tasks
- ~0.93 accuracy & strong BLEU/ROUGE metrics
- Achieved ~90% accuracy / 96% recall using engineered features
- Interpretable feature importance & risk drivers
GPA: 3.78 / 4.00
📜 Official Degree Verification
Thompson Rivers University
DataTalks.Club — MLOps Zoomcamp Certificate | Aug 2025
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Oracle — Oracle Certified Foundations Associate: AI Foundations | Oct 2025 — Oracle Cloud Infrastructure (OCI)
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I'm open to opportunities in ML Engineering, Data Science, and MLOps across Canada & the U.S.
📧 Email: ajdedenuola@gmail.com
💼 LinkedIn: https://www.linkedin.com/in/jeremiah-dedenuola
💻 GitHub: https://github.com/JDede1
📄 Résumé: View PDF
✨ Thanks for visiting! My work focuses on building ML systems that are transparent, reliable, and production-ready.