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Senior Data Scientist
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Senior Data Scientist with 11+ years of experience building Machine Learning models and ETL data pipelines.
University of Connecticut School of Business
Masters in Business Analytics and Project Management
January 1, 2017 – January 1, 2018
Jawaharlal Nehru Technological University
Bachelor of Technology (B.Tech.), Electrical, Electronics and Communications Engineering
January 1, 2007 – January 1, 2011
Vanguard
Senior Data Scientist
April 1, 2025 – Present
Oracle
Senior Data Scientist - GenAI/ML
October 1, 2022 – April 1, 2025
Cerner Corporation
Data Scientist
November 1, 2018 – October 1, 2022
Kansas City, Missouri Area
SecureHomeIoT
Data Science Intern
February 1, 2018 – April 1, 2018
Hartford, Connecticut Area
Romp n' Roll
Data Science Consultant (Capstone Project)
August 1, 2017 – December 1, 2017
Hartford, Connecticut Area
Tata Consultancy Services
IT Analyst
February 1, 2012 – January 1, 2017
Hyderabad Area, India
AI model for feature prioritization
November 1, 2024 – November 1, 2024
▪ Built and fine-tuned LLM prototypes using Gen AI models (GPT-4) and other BERT-based AI models for customer feedback analysis, sentiment analysis, and personalized recommendations, leveraging LLM embeddings to boost customer engagement and satisfaction ▪ Implemented Aspect-Based Sentiment Analysis using an LLM, DistilBERT AI model, to extract granular insights from customer feedback data, enabling targeted product improvements and driving business decisions for feature prioritization
Predicting probability of stroke happening to Patients - Analytics Vidhya Competition - McKinsey Analytics Online Hackathon - Healthcare Analytics (Won 3rd place out of 2,255 participants)
April 1, 2018 – April 1, 2018
Skills: Machine Learning · Data Analysis
Image classification using Random Forest
November 1, 2017 – December 1, 2017
• Built a Random Forest model to predict the type of fashion article from images • Applied clustering techniques to group similar pixel intensities of the images • Applied grid search technique to tune the hyperparameters • Performed cross-validation to evaluate the performance of the model Tools/libraries used: Python, Pandas, Scikit-learn
Predicting Gender using Profile information collected from websites
November 1, 2017 – December 1, 2017
• Applied NLP techniques and developed new features, and Bag of Words model from username and profile descriptions to predict users’ gender • Built Logistic regression model, Naïve Bayes model, Support Vector Machines and Random Forest model, and evaluated them to select the best model Tools/Libraries used: Python, Sklearn, NLTK
Predicting Customer Churn - Travelers Data Challenge
October 1, 2017 – November 1, 2017
➢ Achieved an accuracy 3.5% higher than the company’s best model, and presented business insights about the drivers of customer retention to the company ➢ Performed feature engineering, feature selection and built a simple Logistic regression model to beat the company's existing GBM model Tools/Techniques used: R, Decision Trees, Logistic Regression
Predicting customers' future purchases
July 1, 2017 – July 1, 2017
Predicting customers' future purchases ➢ Analyzed large dataset (32 million rows), prepared aggregated data and performed feature engineering ➢ Built xgboost model to predict future buys based on customers’ previous purchases Tools/libraries used: R, ff, dplyr
Mercedes Benz Greener Manufacturing - Kaggle Competition
June 1, 2017 – July 1, 2017
• Achieved an R Squared value of 0.546, to predict the time a car takes to pass testing; Winner scored 0.555 • Performed feature engineering, dimensionality reduction, custom-made backward elimination algorithm and feature selection
Quora Question Pairs - Kaggle Dataset
March 1, 2017 – May 1, 2017
• Built xgboost model to predict whether a question pair is a duplicate or not • Used NLP techniques to prepare Bag of Words model, performed POS tagging using parallel processing in R and developed new features based on words and their similarities Tools/Libraries used: R, data.table, NLP, tm, RDRPOSTagger, foreach, doParallel , doSNOW
IBM Certified Designer - Cognos 10 BI Reports
IBM
June 24, 2026 – Present
IBM Certified Solution Developer - InfoSphere DataStage v8.5
IBM
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, including hackathons and Kaggle competitions, suggests a proactive and continuous learning mindset. The transition from IT Analyst to Data Scientist, and then Senior Data Scientist, indicates career growth and adaptability. The experience with GenAI/ML at Oracle aligns well with innovative environments. However, the target role is 'Data Analyst', while the candidate's experience is heavily skewed towards 'Data Scientist' and 'Senior Data Scientist', which might indicate a potential mismatch in role expectations or a desire for a more advanced analytical role. The certifications are in older BI/ETL tools, which might not directly align with a modern Data Analyst role focused on advanced analytics.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a results-oriented approach and problem-solving skills, particularly in competitive settings. The ability to present business insights suggests good communication of technical findings. However, without specific assessment data on soft skills, a comprehensive evaluation of operational fit is limited.