Pagie Thomas
Data Scientist and AI/ML Solutions Architect in Washington DC Metro Area
I am an enterprise Data Scientist and AI/ML Practitioner with over a decade of experience delivering production-grade analytics and scalable data products. My work spans distributed big data architecture, predictive modeling, and Generative AI, with a consistent focus on turning complex, ambiguous data into concrete systems that reduce operational costs and accelerate insight delivery.
At the Principal level, my value is not just in building models, but in shaping decision-support systems that organizations can trust, govern, and scale. I design with real-world constraints in mind—imperfect data, cross-team dependencies, and existing workflows—ensuring technical solutions drive sustained stakeholder adoption rather than succeeding in isolation.
🚀 ENTERPRISE IMPACT HIGHLIGHTS:
• GenAI & NLP at Scale: Designed and deployed a production Generative AI and RAG-based pipeline analyzing 10K+ free-text records, reducing manual review time by 80% and accelerating insight delivery by ~40 days.
• Workflow Predictive Modeling: Built PyTorch and statistical deep learning models that flagged ~45% of backlogged procurements as redundant, driving a 30% gain in supply chain efficiency.
• Leadership Decision Support: Developed interactive enterprise dashboards for cross-functional teams, increasing leadership visibility and cutting manual reporting burdens by 75%.
🔧 TECHNICAL CORE:
Databricks (Serverless, Unity Catalog), PySpark, Spark SQL, Python (Scikit-Learn, PyTorch), AWS (SageMaker), SQL, Tableau, MLOps Pipelines.
💡 MY WORKING PHILOSOPHY:
"Data scientists are collaborators, not heroes. First, lock down the metrics that matter, then build something resilient, clear, and exceptionally valuable to the end user."