I am an aspiring data scientist with a Master's in Applied Mathematics (Data Science Track) from Northeastern University. Currently, I am seeking full-time roles starting immediately.
I am passionate about Machine Learning, Deep Learning, statistical modeling, and Bayesian Learning. I aim to solve problems by leveraging my industry experience and Math skills along with my Data Science knowledge to deliver efficient and effective solutions.
I worked as a Data Science Co-op at Peapod Digital Labs. One of my projects was building a predictive model to forecast customer behavior based on historical data. I led a team of 3, analyzed data for 100k customers and implemented XGBoost Regression using R and achieved 76% R2 value.
I interned at Woods Hole Oceanographic Institution as a Guest Student Investigator where I portrayed my data analysis skills. I studied the Ocean-Atmosphere coupling in the Indian Ocean and the Pacific Ocean and carried out Spatiotemporal Analysis to explore it.
Predictive Analytics for Home Health Monitoring Device- Data Science Intern
Conducted market research, need and return analysis for MouthLab, manufactured by Multisensor Diagnostics. Trained logistic regression model to predict COPD exacerbation utilizing several parameters obtained from MouthLab.
I worked as a Graduate Teaching Assistant for the Machine Learning Course at Khoury College of Computer Sciences
Constructed a deconfounder based on David Blei’s “Blessings of Multiple Causes” to estimate true effects of each actor on a movie’s revenue. Built a Probabilistic PCA model in Pyro to infer substitute confounders.
Predicting Final Player Rank in PUBG (Multi-Player online game)
Implemented multi-class classification using Naïve Bayes from in-game stats and initial player ratings to predict the
Win Place percentile with an accuracy of 61%. Constructed a neural network and improved accuracy to 73%.