About

Shubham Bansal is a Full Stack Machine Learning Engineer and Manager with over six years of experience in the software industry. He specializes in designing, building, and deploying scalable Machine Learning systems and solutions to solve complex business problems. Shubham has experience leading end-to-end machine learning projects, from the initial business justification to a fully functional deployed model making real-time inference. Shubham Bansal is a strong research professional with a Master of Science in Computer Science and Statistics - Machine/Deep Learning from the University of Illinois at Urbana-Champaign. He has expertise in languages such as Python, R, Bash, SQL, and C++, and is well-versed in Machine Learning concepts such as Feature Selection and Dimensionality Reduction, Regression, Clustering, Classification, Bayesian Learning, Ensemble Methods, Artificial Neural Network, Model Selection and Assessment, Exploratory Data Analysis, Data Wrangling, Information Visualization, Language Modeling, and Topic Modeling. Shubham has worked with various tools, IDEs, and frameworks such as RStudio, Git, Jupyter, Tableau, MS Office, Anaconda, TeXShop, Elasticsearch, NumPy, Pandas, scikit-learn, BeautifulSoup, Selenium, Requests, Matplotlib, ggplot2, Seaborn, Plotly, XGBoost, Keras, OpenAI Gym, spaCy, NLTK, tidyverse, dplyr, Glmnet, R Shiny, MySQL, MongoDB, and Neo4j. Currently, Shubham Bansal is working as a Manager, Machine Learning at VISA. He has also worked as a Senior Machine Learning Engineer at Walmart and Wayfair. At Walmart, he designed and developed multi-tier highly scalable, high throughput, and low-latency systems using technologies like Pandas, Dask, Ray, and NVIDIA Rapids Distributed Computing frameworks. At Wayfair, he generated 1.5M/3M USD annual profit/revenue by improving product-level demand estimates using a LightGBM regression model and saved 1.5M USD worth of annual investment by adapting a Negative Binomial Regression model. He has also developed an ensemble framework in Python to estimate product-level marginal lift in demand (resulting from price reduction) using Autoregressive and Constant Elasticity models. Shubham Bansal has a Bachelor of Technology in Computer Science and Engineering from MNNIT. His tech stack includes ML, SQL, Jupyter, XGBoost, Python, Pandas,