Srikesh A.
Adjunct Assistant Professor of Biostatistics @Columbia University Irving Medical Center
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WORK HISTORY
Adjunct Assistant Professor of Biostatistics @Columbia University Irving Medical Center
Graduate teaching and mentorship in longitudinal data analysis and causal inference, with applications to RCTs, RWE, and public health; ongoing academic appointment supporting translational and regulatory-grade analytics leadership.
EDUCATION
University of California, Berkeley
Master of Arts (M.A.), Biostatistics
Missouri University of Science and Technology
Master of Science (M.S.), Mechanical Engineering
University of California, Berkeley
Doctor of Philosophy (Ph.D.), Biostatistics
SKILLS
ABOUT SRIKESH A.
Enterprise Data & AI executive (PhD, Biostatistics) 17+ years of leadership across biopharma, biotech, healthcare, and life sciences, building enterprise-scale analytics platforms and teams that turn complex clinical and research data into decision-grade evidence.I have led global data science and AI organizations spanning clinical trials, real-world evidence (EHR/claims), oncology, and translational research—owning strategy, delivery, and governance in highly regulated environments (GxP, 21 CFR Part 11, HIPAA/GDPR). My work has directly influenced study design, portfolio decisions, regulatory submissions, and commercial strategy, while materially improving speed, reproducibility, and scientific rigor (e.g, reducing analytics cycles from months to weeks).I operate at the intersection of science, technology, and execution—partnering closely with clinicians, statisticians, translational scientists, engineering, and business leaders to design scalable AI-enabled platforms rather than one-off analyses. This includes real-world evidence generation, NLP/LLMs on clinical text, trial simulation and optimization (external/synthetic controls), and predictive modeling to support clinical development and health outcomes.Beyond delivery, I build durable capability: hiring and mentoring senior talent, establishing engineering and modeling standards, implementing MLOps/LLMOps, and creating governance frameworks that allow advanced analytics to be trusted, auditable, and production ready.I also maintain strong external partnerships with academia, regulators, and technology providers—evaluating build-vs-buy decisions and structuring data and AI collaborations that de-risk innovation while accelerating enterprise impact: Clinical & Translational Analytics · RWE/EHR · Oncology · AI/ML & NLP/LLMs · Trial Optimization · Cloud/HPC Platforms · Analytics Governance & Compliance
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