Data Service

Sakshi Bhargava

¬ +1-415-***-**** * *********@****.*****.*** sbhargava024

Education

Masters in Analytics July 2015-June 2016(expected) University of San Francisco, San Francisco, CA, GPA: 3.9/4.0 Courses: Machine Learning, Time Series Analysis, NOSQL Databases, Linear Regression, Relational Databases, Data Acquisition, Exploratory Data Analysis, Computation for Analytics, Probability and Statistics, Data Visualization, Distributed Computing, Supply Chain Analytics, Design of Experiments, Web Analytics

Bachelor of Electronics and Communication Engineering Sept 2008–June 2012 IES IPS Academy, India, GPA: 3.6/4.0

Technical Skills

Spark, Python, R, PostgreSQL, AWS, Tableau, D3, SAS, VBA, Teradata, Learning Hadoop and Hive

Machine Learning, Text Analysis, Time Series Analysis, Predictive Modeling, Data Mining, Image Analysis Professional Experience

Data Science Intern November 2015–Present

Williams-Sonoma Inc. (Retail, e-commerce) San Francisco

– Enhanced accuracy of image classification process from 90% to 99%

– Applied graph cut and mean shift algorithm to identify object in an image and predict its color with 91% accuracy

– Recognized patterns in an image using convolutional neural network with 82% accuracy

Senior Business Analyst October 2012–April 2015

Mu Sigma Business Solutions Pvt. Ltd. (Advanced Analytics and Decision Sciences) Bangalore, India Pattern Analysis of Regulatory Visits at Pharmacy Stores

– Predicted likelihood of inspections of stores using logistic regression model. Performed pre-post analysis, hypothesis testing and k-means clustering

– The model had 75% accuracy and had led to 10% reduction in fines due to non-compliance Sales Driver Analysis and Sales Forecasting for Spare Parts of Home Appliances

– Developed regression model to determine the various drivers a ecting orders of spare parts for distribution channels

– Built forecast model using ARIMA to predict orders of spare parts. Automated process using SAS and VBA Import Inventory Management for Retail Stores

– Forecasted import demand using decomposition time series modeling techniques (SAS).

– Prediction methodology improved demand forecast accuracy by 18% Compliance Performance Scorecard

– Developed scorecard in Tableau by defining metrics and their thresholds to assign compliance scores to pharmacies Awards. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Awarded for ’Pattern Analysis of Regulatory Visits’ project for Leading the project Providing thorough and rigorously tested analytical solutions Hard work and dedication Course Projects

Scalable Yelp Recommendation Service: Built a web app to recommend businesses to Yelp users. Used Spark and applied Matrix Factorization, Random Forest, Gradient Boosting models and feature engineering to predict business ratings. Improved RMS error by 8% over the baseline model of average ratings

Kaggle’s "What’s Cooking" Competition by Yummly: Built multiclass classifier using python, to predict cuisine of a recipe, given a list of ingredients. Built an ensemble of logistic regression, random forest and gradient boosting models and achieved accuracy of 80.99%. Finished amongst top 10% (approx 1400 teams)

REST Web Service: Amazon Beauty Products Data API: Created a RESTful web service (hosted on EC2) using Python Flask to retrieve data, stored in AWS RDS. Performed ETL on json data using python and PostgreSQL

Retweet Analysis: Analyzed factors that lead to retweeting and developed a regression model to predict the number of retweets using Python and PostgreSQL, pulling data from the Twitter REST API

Sentiment Classifier: Classified movie reviews as positive or negative with 82% accuracy, by implementing self-coded Naïve Bayes algorithm in both python and pyspark. Enhanced accuracy up to 90% by adding a subjectivity classifier

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