Yash Bansod
Data Scientist @Process Integration Limited
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WORK HISTORY
Data Scientist @Process Integration Limited
Manchester, GB
Lead data science R&D initiatives and serve as primary touch-point for data-oriented solutions, developing bespoke machine learning libraries and in-house tools that leverage unique data and engineering expertise to optimize complex oil and gas processes beyond traditional approaches• Designed novel neural network architectures for uncertainty analysis using Mean Variance Estimation, Bootstrapping, and Bayesian neural networks, combined with SHAP analysis to provide process insights with 94-96% statistical confidence intervals for critical refinery operation decisions• Applied comprehensive machine learning methodologies including supervised/unsupervised techniques (k-means, DBSCAN, PCA for outlier detection, kNN regression) and deep learning with TensorFlow/Keras (MLP/RNN architectures), optimized through strategic feature selection • Built end-to-end data solutions including user-friendly Dash/Python front-end interfaces enabling non-specialists to access advanced ML capabilities, comprehensive data visualization with Plotly, and robust software development lifecycle management using Git/GitHub• Translated complex machine learning outputs into actionable business insights for cross-functional teams and clients, ensuring scalable and adaptable solutions for real-world industrial implementations in the energy sector
EDUCATION
Indian Institute of Technology, Delhi
Dual degree (B.tech + M. tech) , Chemical Engineering
The University of Manchester
Doctor of Philosophy - PhD, Chemical Engineering
ABOUT YASH BANSOD
I am a Ph.D. Chemical Engineer from the University of Manchester specializing in the intersection of sustainable process development and data science. My research established breakthrough achievements in bio-based acrylic acid production from glycerol, achieving the highest yield reported in literature. I bring strong expertise in machine learning, predictive modeling, and data-driven optimization applied to complex chemical processes.With experience spanning academic research and industry roles at pharmaceutical, medical device, and energy companies, I combine deep technical knowledge with practical business applications. I excel at translating complex data into actionable insights through advanced analytics, simulation, and uncertainty quantification. My work is published in high-impact journals focusing on sustainability and process engineering, demonstrating my commitment to sustainable manufacturing processes that balance technical performance with environmental considerations.
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