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vhanjyangchautari@gmail.comPHONE NUMBER AVAILABLEHaddonfield, NJ, Street Address
https://LINKEDIN LINK AVAILABLEhttps://github.com/sunilStreet Address
EDUCATIONUniversity of Alabama in Huntsville Aug 2017  May 2022Ph.D. in Physics (Astrophysics) Huntsville, ALG.P.A. 4.0/4.0Relevant coursework: Data Analysis Math I & IISKILLSProgramming: Python, R, SQL, BashOptimization: Gurobi, PyomoMachine Learning: Cross Validation, PCA, Logistic Regression, KNN, Random Forest, Gradient Boosting, SVM, K-means ClusteringData Science Tools: Pandas, NumPy, Scikit-learn, Keras, TensorFlow, PyTorchVisualization: Matplotlib, Seaborn, Plotly, TableauDevOps Tools: Jira, Confluence, Jenkins, ELK, Git, GitHub, SourceTree, postman, PyCharm, Data BricksEXPERIENCEClient: Comcast Corporation Sep 2022  March 2024Data Scientist/Python Developer Philadelphia, PACustomized channel distribution with SQL, increasing solver speed by threefold and reducing compute costsfor Wi-Fi mesh network.Spearheaded the design, development, and deployment of ML solutions to optimize business decisions, saving$1M in workforce expenses by accurately forecasting radio channels.Architected an implemented an ensemble model integrating Scikit-learn random forest and XGBoostalgorithms,, achieving a remarkable 97% accuracy in predicting pipe seam type.Achieved 95% info retention by reducing data dimensionality from 27 to 15 features for 30k points with PCA.Leveraged operational data sources and optimization techniques to create tools for developing scenarios withcost and enrollment optimization, delivering related real-world data insights.Delivered actionable insights to senior management through compelling data visualization and comparativeanalysis of 1M+ observations, facilitating data-driven decision-making process using Matplotlib, Plotly, Tabeau.Collaborated with cross-functional teams to understand business requirements and translate them intopractical ML solutions.University of Alabama in Huntsville (UAH) Aug 2020 - May 2022Research Aide/Research Assistant Huntsville, ALFeatured on the cover page of Nature Astronomy, showcasing a significant galaxy mosaic crafted with Pythonlibraries including Pandas, Seaborn, and Plotly, leveraging 100 Gigabytes of Hubble Space Telescope Data.Built a machine learning model (Laplacian Edge Detection Algorithm) to remove Cosmic rays and artifacts fromHubble Space telescope data, reducing computation time by 10 min per filter (image).Developed a tracking system for nearby galaxies with Astroquery (like SQL) to improve catalogue accuracy,reducing error by 20%.Enhanced data quality by 17% through cleaning and preprocessing using a comprehensive suite of Pythonlibraries, including NumPy, Pandas, Scikit-learn, and additional tools.Client: BBVA Bank Feb 2017 - May 2019Junior Data Scientist Birmingham, ALAutomated classifier models like Random Forest, SVM for specific segments of a customer base, saving 22hours of labor per month.Constructed operational reporting and data visualization tools, reducing contractor scheduling costs by10% inthe annual budget.Deployed Auto-Sklearn to automate machine learning model selection, reducing modeling time by 2 hours persession.Devised scalable solutions for Amazon EC2-based cloud environments, boosting storage efficiency by 20% andaccelerating data analysis tools processing speed by 10% within AWS infrastructure.Adapted configurations to align with client requirements, resulting in a positive increment in systemfunctionality and a 7% improvement in overall performance.TRAININGPragmatic Institute May 2022 - July 2022Data Science Fellow RemoteEmployed NLTK on thousands of scraped Reddit posts to train classification models, reaching 92% accuracywith the top-performing model (Nave Bayes with Count vectorize).Forecasted the success of bank marketing campaigns using various machine learning techniques. The bestmodel (Logistic regression) achieved 92% accuracy, 93% precision, and 97% recall.Developed multiple ML models for predicting customer churn in the European banking industry, with theRandom Forest model demonstrating the best performance (F1=87%, recall=83%, precision=91%).Achieved an average classification accuracy of 90% using Natural Language Processing (NLP) techniques,including Count Vectorizer/Hash Vectorizer, Term Frequency-Inverse Document Frequency (TF  IDF),Tokenizing/Stemming, Multinomial Nave Bayes for categorizing into various genres.