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Updated 2 years ago

Summary

Masters candidate in data science currently interning at United Healthcare Group. 3+ years of experience in building end-to-end data-driven products. Strong background in statistics and machine learning. Interested to apply Machine Learning and Deep Learning skills to industry to solve exciting new problems.

Skills

  • Product Management
  • Python
  • R Programming
  • SQL

Education

July 2017 August 2018

Masters of Data Science at University of San Francisco

Relevant Coursework:
Machine Learning, Distributed Computing, Time Series Analysis, Probability and Statistics, Linear Algebra, Data Acquisition, Linear Regression, A/B Testing, Computational Statistics

Technical Skills:

Models/Algorithms
Random Forest, Gradient Boosting Models, Clustering (K-Means, Spectral), Collaborative Filtering, Linear and logistic regression, Neural Nets (CNN, RNN, LSTM)

Databases/Framework
Spark, Pytorch, Keras, MongoDB

Languages
Python, R, SQL

August 2010 July 2014

B.Tech. in Aerospace Engineering at Indian Institute of Technology, Madras

Relevant coursework:
Calculus, Differential Equations, Linear Algebra & Numerical Analysis, Process Optimization, Complex Variables and Transform Techniques, Multivariate Data Analysis for Process Modeling, and Probability, Statistics & Stochastic processes

Experience

October 2017

Data Scientist Intern at UnitedHealthcare Group

Project: Categorizing customer reviews
• Identified and quantified main customer issues from freeform web surveys responses through topic modeling
• Engineered features using natural language processing techniques, implemented classification model on web survey data to identify key factors that affect the net promotion score (NPS)
Project: Rejection Claims Analysis
• Identified business-critical factors that cause long waiting time for an approval of health insurance claim, by experimenting with features and machine learning models
• Predicted the resolution time of an insurance claim with an accuracy of 85% with a 5-layer Deep Neural Network
• Processed ~620K customer episodes from 18M claims transactions based on key business metrics using SQL

February 2016 June 2017

Data Scientist, Forecasting at Merck Sharp & Dohme Pharmaceutical

Python: Pandas, Numpy, Scipy, Scikit-learn, PyQt; Techniques: Time Series Analysis
Project: Application development
• Developed an automated data cleaning and curve fitting tool based on Bass Model Diffusion curves using Python
• Built a web-based forecasting tool with interactive data visualization using R-Shiny that implements automated selection of forecasting techniques such as Holt-Winters, Box Jenkins (SARIMAX, VARX), exponential model

June 2014 January 2016

Business Analyst at Aspect Ratio, Global Centre for Analytics and Forecasting

Tools: Excel, VBA, Spotfire
Project: Oncology therapeutic area
• Led a team of 4 to design, develop and deploy a survival model to understand retention of patients in multiple patient cohorts; received a Five Star Award of Excellence from the client
• Conducted an Exploratory Factor Analysis to identify the underlying relationship between various parameters of launch product. Identified an analog based on similarity of its parameters with the launch product