Data Python

SHRUTI GADKARI

**C Smith Street, Boston, MA - ***20 *******.******@*******.*** 857-***-**** LinkedIn GitHub

Aspiring Data Scientist with 2+ years of industrial experience & expertise in data analytics, predictive modeling & visualization

EDUCATION:

Northeastern University, Boston, MA Master of Science in Data Analytics Engineering (GPA 3.75) May 2020

University of Pune, Pune, India Bachelor’s in Information Technology (GPA 3.67 ) June 2015

Relevant Courses: Big Data, Statistical Analysis, Enterprise Data Management, Neural Networks, Deep learnings, Data Mining, Computation & Visualization for Analytics, Data Structure & Files, Information Retrieval, Distributed systems

TECHNICAL SKILLS:

Programming Skills: Python(NLTK, Numpy, Pandas, scikit-learn,PyTorch,keras, Tensorflow), R(dplyr, tidyverse), Java, Linux

Databases: MySQL, NoSQL, MongoDB, PostgreSQL, Toad,Microsoft SQL Server, Google BigQuery, Oracle SQL developer

Hands-on Technologies: Map-Reduce, Apache Hadoop, Apache Spark, REST API, XML, JSON, GitHub

Tools: RStudio, Microsoft Excel(Pivot Tables, VLOOKUP & macros), Domino, Alteryx, Jupyter Notebook, Anaconda, tomcat

Data Visualization: Tableau, Power BI, Google Analytics, ggplot, Matplotlib, RShiny, Flourish, DataWrapper, jQuery

Soft Skills: Excellent Verbal and Written Communication Skills in English, Collaborative, Innovative

Certificates: Big Data Developer Program, Google Analytics for Beginners

PROFESSIONAL EXPERIENCE:

Data Science Intern, Red Hat, Raleigh NC May 2019 – Dec 2020

•Built NLP AI bot to summarize & multi-label product issue email using state-of-the-art method BERT in Pytorch framework

•Reduced manual involvement by 52% using NLP AI Bot to build Customer Relationship Management Strategies

•Worked on Amazon AWS Redshift Database to prepare data for NLP Bot and deployed this bot on a JBoss web server to produce periodical reports to address product issues that can ease decision making process for business stakeholders

•Developed Python scripts to automate GIT actions on GitLab to speed up DevOps operations for their Enterprise team

•Implemented a predictive model using Linear Mixed Modeling to evaluate product sale variations for different industries

Teaching Assistant, Northeastern University, Boston MA Jan 2019 – May2020

•Conducted collaborative sessions to help students in developing skills for visualization in analytics using tools Tableau, Power BI, cloud-based tools like Flourish & DataWrapper & EDA libraries like ggplot, RShiny, matplotlib, Flask, Django

•Assisted professor in designing a coursework along with grading and independently supervising a class of 40+ students

Senior Software Engineer(Data Analytics), Persistent Systems, Pune, India Oct 2015 – Dec 2017

•Performed ETL for data transformation & wrangling using SQL & Oracle db and used Oracle Business Intelligence Publisher to prepare dashboards to showcase web analytical repots for US Bank Web application hosted in scalable architecture

•Implemented policies for Risk based authentication & user behavior analysis to serve 1000+ Single Sign-on requests simultaneously in multi data center architecture for web application of investment banking client(Great West Financial)

•Designed & configured Data Replication topologies in multi data center architecture for databases of ecommerce retail web application of Safeway to maintain 100% consistency within data centers

•Reduced manual involvement by 80% by developing Perl scripts for automating testing of APIs on servers

ACADEMIC EXPERIENCE

Predictive Models for determining Sales likelihood (Random forest, Gradient Boosting) May 2020

•Implemented models using Random Forest, Logistic Regression and Gradient Boosting to classify a lead into sale/no sale

•Computed probabilities to prioritize call using most accurate classifier to indicate likelihood of lead to be a sale

Image classification of Malaria blood cells (python, tensorflow, CNN) Mar 2020

•Built Convoluted neural network & fine-tuned pretrained VGG19 for malaria cell image classification with accuracy of 96%

Predictive analysis for Preowned car pricing using Python Feb 2019

•Built Linear Regression model to predict price of preowned cars using feature engineering to achieve accuracy of 56%

Diabetes in Indian Women: Analysis using SAS, ANOVA, Tukey’s test, Duncan’s Test Jan 2019 • Performed Anova, Duncan's & Tukey's test to find statistical differences in groups to get effective group for target

Marketing Strategy for Google Merchandise store for the holiday season (R, Logistic Regression) Dec 2018

•Built 5-fold cross-validated model to find online visitor trends to become customers with classification accuracy of 89%

Boston Crime Data Analysis using Tableau Nov 2018

•Developed dashboards to showcase the analysis of crime rate in Boston & predicted future crime rate in Tableau

Stock Price Forecasting using Time Series Predictive models using R Jun 2018

•Predicted Stock prices using multiple time series forecasting models & achieved highest accuracy 65% with ARIMA model

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