Data Scientist bridging 15+ years of pharmaceutical expertise with cutting-edge GenAI & LLM engineering — building production AI systems that accelerate drug development and drive commercial decisions.
Production-grade AI/ML projects spanning pharma R&D, GenAI engineering, and commercial analytics.
ResNet/EfficientNet models for Phase II clinical trial cellular analysis. Achieved 99% accuracy, reducing manual analysis from months to minutes.
View on GitHub →LangChain/LangGraph agentic AI system for enterprise pharma data querying. Multi-step reasoning over clinical databases with full regulatory compliance.
View on GitHub →LSTM time series models for pharmaceutical sales forecasting and customer churn prediction, incorporating PK/ADME domain features. R² above 0.90.
View on Kaggle →ML model using drug-drug interaction and ADME features for interaction risk prediction. Domain-enriched feature engineering delivering 25–30% accuracy gains.
View on Kaggle →Transformer-based NLP pipeline extracting structured clinical signals from 10,000+ adverse event reports and scientific literature for Oncology/Immunology programs.
View on GitHub →LSTM, XGBoost & Ensemble forecasting for inventory optimization; RFM/Cohort-based Customer Lifetime Value models with A/B testing and Sentiment Analysis.
View on GitHub →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 that extract insights from regulatory documents.
The result? AI that actually understands the science — not just the data.
Built HIPAA-compliant LLM agents (LangChain/RAG) for regulatory use cases including automated adverse event report drafting. Deployed to 50+ users across regulatory, medical affairs, and market access teams with 40%+ productivity gains.
Managed end-to-end ML lifecycle ensuring FDA 21 CFR Part 11 compliance and European Pharmacopoeia standards. Implemented human-in-the-loop oversight for high-risk AI use cases impacting regulatory decisions.
Contributed to 10+ European regulatory submissions at OctaPharma for Human Albumin, Factor products, and vaccines. Prepared stability, bioequivalence, and CMC documentation — all successfully approved.
Supporting Oncology drug development at Mentor R&D with AI/ML initiatives. Experience across Oncology, Immunology, Cardiovascular, and Diabetes therapeutic areas.
Developed NLP solutions (Transformers/Hugging Face) to extract insights from 10,000+ unstructured documents including PubMed articles, FDA reports, and regulatory correspondence. Built automated alert systems for regulatory and medical affairs teams.
Ensured all AI-generated outputs maintain complete auditable trails to source documents. Implemented metadata tagging and vectorized text approaches for regulatory data traceability.
From data pipelines to production deployment — with a pharma domain layer no generic data scientist can replicate.
15+ years of industry experience across pharmaceutical R&D, data science, and AI engineering.
Open to pharma/biotech and tech opportunities. Let's talk about how AI can accelerate your drug development or commercial analytics.
📍 Princeton, NJ | +1 929 840 4971 | US Citizen