Data Scientist with 15+ years of pharmaceutical expertise and 6+ years building production GenAI/LLM solutions — delivering 90% cost savings, 40% productivity gains, and 99% model accuracy across clinical and commercial environments.
I started my career as a clinical pharmacist, spending years understanding how drugs behave in the body — pharmacokinetics, drug interactions, ADME. That domain knowledge turned out to be my greatest ML asset.
Today I build production-grade AI systems for pharmaceutical R&D and commercial operations: LLM agents that query clinical databases, CNN models that analyze cell images for Phase II trials, and RAG pipelines powered by LangChain and LangGraph for enterprise Agentic AI workflows.
My MLOps & DevOps stack spans Docker, CI/CD pipelines, AWS SageMaker, FastAPI, and end-to-end model deployment and monitoring — ensuring every model moves cleanly from experiment to production. I work fluently across Python, SQL, and R, with deep experience in IQVIA LAAD, Xponent, Veeva, and omnichannel pharma commercial data.
At IMEBRANDS I'm delivering AI-powered demand forecasting, recommendation engines, and CLV models — proving pharma AI skills translate powerfully to commercial analytics. The result? AI that actually understands the science — not just the data.
Built HIPAA-compliant LLM agents (LangChain/LangGraph/RAG) for enterprise Agentic AI workflows and Next Best Engagement analytics. Deployed to 50+ stakeholders with 40%+ productivity gains.
Extensive hands-on MLOps using Docker, CI/CD pipelines, Git, AWS SageMaker, and FastAPI for real-time model inference. End-to-end lifecycle management from data ingestion to production monitoring.
8+ years daily immersion in IQVIA LAAD, Xponent, Xponent PlanTrak, DDD, Veeva, and Claims/EMR data — shaping pricing decisions, sales force strategy, and physician targeting at the executive level.
Drove NLP/Transformer pipelines for adverse event analysis across Oncology and Immunology programs. Experience also spans Diabetes, Obesity, and Cardiometabolic therapeutic areas.
Managed ML lifecycle ensuring FDA regulatory compliance, data governance standards, and human-in-the-loop oversight across all production deployments. Contributed to 10+ EU regulatory submissions.
Production-grade AI/ML projects spanning pharma R&D, GenAI engineering, and commercial analytics.
ResNet/EfficientNet deep learning models for Phase II clinical trial cellular analysis. Reduced manual review from months to minutes. Achieved 99% accuracy delivering 90% cost savings for drug development timelines.
View on GitHub →LangChain/LangGraph agentic AI system for enterprise pharma data querying. Multi-step reasoning over clinical databases with full regulatory compliance. Deployed to 50+ users with 40% productivity gains.
View on GitHub →LSTM time series models incorporating PK/ADME domain features for pharmaceutical sales forecasting and customer churn prediction. Achieved R² above 0.90, enabling data-driven strategic planning.
View on GitHub →ML model using drug-drug interaction and ADME features for interaction risk prediction. Domain-enriched feature engineering bridging AI with pharmaceutical science delivering 25-30% accuracy gains.
View on GitHub →Transformer-based NLP pipeline extracting structured clinical signals from 10,000+ adverse event reports and scientific literature for Oncology/Immunology programs. Automated alert systems for medical affairs teams.
View on GitHub →LSTM, XGBoost & Ensemble forecasting for inventory optimization at IMEBRANDS. RFM/Cohort-based Customer Lifetime Value models with A/B testing and Sentiment Analysis for marketing strategy.
View on GitHub →7 live deployments across HuggingFace, Streamlit, and Railway.
Have a question or want to collaborate? I'd love to hear from you!