I’ve always been torn between the elegant certainty of mathematics and the beautiful complexity of biology, so I decided to master both. My obsession with healthcare data science began at IIT Delhi, where I spent my Master’s thesis translating the abstract constructs of evolution into mathematical models. That research spark eventually took me to Harvard University, building computational drug discovery frameworks for DARPA-funded projects, followed by a summer at Vertex Pharmaceuticals while finishing Data Science Degree at Columbia University. Today, I’m a Senior Data Scientist on the Experimental AI team at AbbVie, where I use complex statistical theory and AI to build enterprise-ready solutions that aid decision-making for clinical leadership. Whether it’s decoding disease at the discovery stage or building audit-ready AI solutions, I live for the moments where rigorous algorithms meet clinical innovation.
Experience across Drug Lifecycle
Industry Experience
AbbVie Inc (2023 - Present)
Site Anomaly & Fraud Detection
Engineered GLMM-based unsupervised models to identify atypical site behavior and fraud. Deployed portfolio-wide across global trials to ensure high-fidelity data oversight.
Professional Patient Detection
Developed novel algorithms to identify duplicate or "professional" trial participants, safeguarding data integrity across international clinical sites.
Medical Monitoring
Engineered ensemble models for unsupervised patient monitoring, reducing clinical review and medical escalation times by 90%.
Risk-Based Quality Management of Clinical Trials
Implemented enterprise-level RBQM frameworks to optimize monitoring resources. Focused oversight on critical-to-quality data points to maximize trial efficiency.
Wyss Institute, Harvard University (2019 - 2022)
AI Drug Discovery
Developed Markov Random Field frameworks for gene regulatory networks to identify targets and drugs, accelerating therapeutic discovery throughput.
