# Kiran Gauthier > Applied Science @ Ramp Location: New York, New York, United States Profile: https://flows.cv/kirangauthier PhD in optimal experimental design and hierarchical modeling, I leverage AI, Bayesian stats, and causal inference to quantify luck using compute, and understand how information flows through directed acyclic graphs (DAGs) and graphical models. Unfortunately aware of how correlation does not even imply [correlation](https://tinyurl.com/gelman-cor). I mainly write in Python, PyMC, R, Stan, and less often in TFP, Pyro, Keras, TensorFlow, PyTorch, Julia and SQL. I enjoy making sense of the noisy world by quantifying the "value" of both our model and data, and understanding how to adaptively allocate finite resources, given low-quality, heterogeneous signals using lessons from probability and information theory. Feel free to email me (kiran.gauthier@columbia.edu) for any of my [publications](http://tinyurl.com/kiran-scholar) or to connect. ## Work Experience ### Senior Applied Scientist @ Ramp Jan 2025 – Present | New York, New York, United States Credit risk. ### Senior Data Scientist | YouTube Data Science @ Google Jan 2024 – Jan 2025 | New York, New York, United States Global experimental lead for YouTube Shopping Built Google's first causal propensity model to learn "what works for whom" in the context of YouTube Shopping, led to X-XXx in ROI and XX-XX% decrease in program costs by identifying cohorts of creators that can be "nudged" into sustained Shopping success. Focus areas: experimental design & causal inference. Causal propensity modelling | stochastic trees | hierarchical causal models ### Data Scientist @ Bristol Myers Squibb Jan 2022 – Jan 2024 R, Python, and Stan to make data-driven decisions for early stage (Phase I/II) compounds, competitively position assets, and advise on dose schedule using hierarchical modeling and explainable AI / ML. Led a Python based summer internship on the use of GANs for synthetic patient augmentation, and algorithmic covariate selection in oncology trials. ### PhD - Engineering, Computational Statistics @ Columbia University in the City of New York Jan 2017 – Jan 2022 | Greater New York City Area Bishop Lab for Active Materials --- Created an adaptive experimental design framework to maximize the "value" of noisy, nonlinear, hierarchically structured data using probability and information theory. Exploited conditional entropies, sufficient statistics, and efficient marginalizations to render Bayesian inference and design 10-10,000x faster than a naive implementation to make principled DoE accessible on experimentally relevant timescales. ### Undergraduate Researcher @ Brown University Jan 2015 – Jan 2017 Shukla Lab for Designer Biomaterials --- Engineered an oxygen-permeable, drug-loaded hydrogel designed for burn wound victims to prevent hypoxia / infection of the site. Our therapy provided a much longer term application than conventional therapies, which significantly damage new tissue growth upon removal. Collaborated with researchers at Harvard University to conjugate metalloporphyrin to computationally quantify oxygen flux, giving doctors a visual indicator of the time-varying oxygen permeability. ## Education ### Doctor of Philosophy - PhD Columbia University ### Master's degree in Chemical Engineering Columbia University ### Bachelor of Engineering (B.E.) in Chemical Engineering Brown University ### Diploma International Baccalaureate ## Contact & Social - LinkedIn: https://linkedin.com/in/kiran-gauthier --- Source: https://flows.cv/kirangauthier JSON Resume: https://flows.cv/kirangauthier/resume.json Last updated: 2026-04-07