Data Science | Marketing | Google | Ex - Meta
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W. P. Carey School of Business – Arizona State University
Master’s Degree, Business Analytics
January 1, 2015 – January 1, 2016
ABV-Indian Institute of Information Technology and Management
Integrated Post Graduation (B. Tech + M. Tech), Information Technology
January 1, 2008 – January 1, 2013
Data Scientist - Media Lab
December 1, 2023 – Present
Greater London, England, United Kingdom · On-site
Meta
Data Scientist (Platform Moderation & Marketing Science)
June 1, 2020 – July 1, 2023
Austin, Texas, United States · On-site
Capital One
Principal Data Scientist | US Card Marketing
August 1, 2017 – June 1, 2020
Richmond, Virginia Area
Constant Analytics, Inc.
Manager, Analytics
March 1, 2017 – July 1, 2017
Atlanta Metropolitan Area
Constant Analytics, Inc.
Senior Analytics Consultant
August 1, 2016 – February 1, 2017
Atlanta Metropolitan Area
ZS Associates
Business Analytics Associate
March 1, 2014 – June 1, 2015
Gurugram, Haryana, India
Mu Sigma
Decision Scientist
June 1, 2013 – March 1, 2014
Bangalore, India
ABV- Indian Institute of Information Technology and Management
Teaching and Research Assistant (Digital Image Processing)
June 1, 2012 – June 1, 2013
Greater Gwalior Area
InterpretOmics
Summer Internship
May 1, 2012 – June 1, 2012
Bangalore
Stochastic Optimization Modelling Case Project - UNICEF RUTF Supply Chain Optimization
February 1, 2016 – Present
Our team developed and analyzed a simulation model of ready-to-use-therapeutic food (RUTF) procurement policies at the UNICEF country office in Kenya. We identified areas for improvement to the system by identifying KPI's in the supply chain and used linear programming, simulation tools, and solver add-ins for insight. We forecasted seasonal and weekly demand for RUTF in Kenya using regression, moving averages and extrapolation techniques. Through our analysis of sensitivity models we were able to offer insights and recommendations for the improvement and optimization of the RUTF supply chain.
Prudential Life Insurance Assessment - Kaggle Competition
January 1, 2016 – April 1, 2016
Prudential, one of the largest issuers of insurance in the USA, wants to develop a predictive model that accurately classifies risk using a more automated approach instead of customers providing extensive information to identify risk classification and eligibility, including scheduling medical exams, a process that takes an average of 30 days. We have been working on this project since January and the findings is as described below: - Performed Exploratory Data Analysis (EDA), and initial data munging. This process involved doing correlation analysis, regression analysis to find out the attributes which are highly correlated so that special attention is given to those specific attributes. - Performed exploratory data analysis (EDA), data pre-processing which basically involved imputing missing values with median, Maximum Likelihood Estimation (MLE). - Implemented standalone classification models such as CART Tree, SVM, KNN, Logistic Regression, Neural Nets, Naive Bayes Classifier and ensemble models such as Random Forest, XGBoost, and ADABoost. - Scored an accuracy of 59%, with the best accuracy in the kaggle competition so far to be 67% - Did Parameter Tuning (cross-validation) to have a better model - Deduced the way to convert the classification problem into regression problem using Genetic Algorithm (Label Space Dimensionality Reduction) and found better accuracy of 60%, better than any other classification models.
Predicting Happiness Index
November 1, 2015 – Present
With the survey, Smarticon government has access to more than 13000 responses. We built a model to predict the happiness levels of the citizens to help build policies on those classes.
Otto Group Product Classification Challenge
November 1, 2015 – Present
As part of Data Mining 1 course, executed the Otto Group Product Classification problem in Kaggle. The goal of the project was to correctly classify the products into one of the 10 categories. Tools used were Azure ML, R and the algorithms tried out were Random Forest, xgboost, Neural Network. Best results were obtained using xgboost.
Retail stores - Inventory optimization problem
September 1, 2015 – Present
The goal of this project is to optimize the inventory by effectively using the NewsVendor model to forecast the demand for the Retail store.
Coronary Artery Stenosis Detection from Cardiac Angiography using Image Processing and Parallel Architecture
May 1, 2012 – May 1, 2013
We have automated the process of detecting stenosis location in the Coronary angiography. Our application takes angiography images as input and produces a report in which stenosis regions are marked with the percentage of blockage in the artery. Technologies and skills used are: - Digital image processing, - Cardiology and Radiology (inputs from Doctors) - Fuzzy logic - MATLAB Image processing toolbox
Automated Traffic Management Using Image Processing
April 1, 2012 – Present
A real time automated traffic signal control system was developed with traffic management feature. The system automatically adjusts the traffic lights according to the incoming traffic and it also considers the traffic diverting from the neighborhood traffic signals and adjusts the signal timings accordingly.
Navigation of an Automated Guided Vehicle Using Image Processing And Isolated Word
January 1, 2009 – April 1, 2011
A practical implementation of Automated guided vehicle has been developed using Robotics, image processing and isolated word recognition. A modeled vehicle has been driven autonomously on the road and can navigate on a road on its own, stops at crossing, follow traffic lights and reach an end point or can be controlled through voice commands.
Cultural Fit Analysis
The candidate's career trajectory through prominent companies like Google, Meta, and Capital One, combined with a diverse portfolio of analytical projects, suggests a strong fit for data-driven, innovative environments. The variety of projects, from supply chain optimization to image processing and financial risk assessment, indicates intellectual curiosity and a willingness to tackle different challenges. This breadth of experience aligns well with organizations that value versatile data professionals. The target role of 'Data Analyst' is well-aligned with the candidate's extensive experience in data science and analytics, suggesting a strong cultural fit for roles requiring deep analytical rigor.
Soft Skills & Operational Fit
The candidate's project descriptions suggest strong problem-solving abilities and a results-oriented approach. Experience in diverse roles from individual contributor to manager indicates adaptability and potential for leadership. The nature of the projects implies a capacity for critical thinking and translating complex data into actionable insights. However, without specific behavioral or psychometric test results, a detailed assessment of soft skills like teamwork, communication style, or stress handling is not possible.