Engineer Data

SHUBHAM SHARMA

Chicago, IL 312-***-**** ********@***.*** www.linkedin.com/in/shubham-sharma-uic

PROFESSIONAL SUMMARY

Data Scientist with a solid Math background and 4 years of professional experience in the Information technology and services industry. Google certified - Associate Cloud Engineer and skilled in Machine Learning, Predictive Modeling, and Data Mining to derive insights from data to drive successful business decisions. SKILLS

Technical Skills: Machine Learning, Deep Learning, Predictive Modelling, Data Visualization, Google Cloud Platform, Data Science Libraries: Pandas, NumPy, TensorFlow, Scikit-learn, Keras, matplotlib, NLTK, ggplot2, Tidyverse Languages and Tools: Python, R, MySQL, PyCharm, Tableau, MS Excel, BigQuery, Unix Shell, Git, Slack, Confluence, JIRA EDUCATION

University of Illinois at Chicago - College of Business Administration December 2020 Master of Science in Business Analytics GPA: 3.72/4 Jaypee University of Engineering and Technology, MP, India Bachelor of Technology in Mechanical Engineering

PROFESSIONAL EXPERIENCE

JamFeed Austin, TX May 2020 - Present

Data Science Intern GCP, MySQL, Cloud Dataflow, BigQuery, TensorFlow

• Natural Language Processing: Developed and deployed a Machine Learning model on AI platform to automatically categorize and tag incoming music news articles using NLP text classification and entity extraction, saving 41 man-hours

• Data Engineering: Automated ETL processes using Cloud functions, Cloud storage and Cloud scheduler making it easier to ingest and wrangle data thus reducing time by as much as 60%

• Data Visualization: Developed dashboards and visualizations using Data Studio to monitor important KPI

• Recommendation System: Built a recommendation system using TensorFlow to suggest new music artists and articles using Collaborating filtering. This helped in increase in JamFeed premium subscription by 12%

• Project Management: Ensured timely updates to the stakeholders using tools like JIRA, GIT, Slack and Confluence Ekta Flow Chicago, IL January 2020 - May 2020

Data Science Intern - Capstone Python, Scikit-learn, Tableau, SQL

• Supervised Learning: Built an XG-Boost model to predict the probability of an individual to donate, volunteer or mentor

• Principal Component Analysis: Dimensions were reduced, and important features were extracted using PCA

• Unsupervised Learning - Clustering: Implemented an unsupervised learning algorithm (k-means clustering) to segment target population into subgroups of similar people to develop personalized marketing strategies Tata Consultancy Services Mumbai, India March 2015 - October 2018 Systems Engineer (Application development, support and maintenance) SQL, Excel, SAS

• Database management: Managed and upgraded Configuration management databases and Compliance risk profiles

• SAS: Automated analytics report generation using SAS macros to reduce manual errors, saving 80 man-hours each week

• SQL: Created SQL scripts to analyze data, modify data, import/export scripts, and execute stored procedures

• Achieved 15% increase in same day completion rates and 8% drop in SLA breach requests ACADEMIC PROJECTS

Named Entity Recognition using Long-Short Term Memory (LSTMs) with Keras Python, TensorFlow, Keras May 2020

• Bi-directional LSTMs were used to extract entities like names, locations and monetary values from Kaggle NER dataset

• After running 25 epochs with 1000 batch size, the final accuracy was 0.9687 Image classification using Neural Networks Python, Keras, TensorFlow October 2019

• Developed a Neural network image classification model to classify images into specific categories using Keras

• Implemented transfer learning with ResNet50 model and tuned it using Cyclical learning rate, Image augmentation, Sane weight initialization, Image cleaner and dropout techniques to develop model with an improved accuracy of 89.1% Credit Risk Modelling Python March 2019

• Predicted the probability of default of an applicant using applicant’s demographic, financial and credit history details

• Scaling, label encoding and missing value imputation techniques were used to pre-process the data

• XG-Boost gave the best classification accuracy of 79%. Hyperparameter tuning was used to achieve higher accuracy Sentiment Analysis of Yelp Restaurant reviews Python, Scikit-Learn, matplotlib, NLTK February 2020

• Built an NLP based classification model to predict sentiments given text review of a restaurant

• Word embedding, tokenization and padding techniques were used to extract sentiments out of reviews CERTIFICATES AND ACTIVITES

• Google Cloud Certified - Associate Cloud Engineer June 2020

• Cloud Engineering with Google Cloud Specialization - Professional Certificate by Google Cloud April 2020

• TensorFlow Developer Professional Certification by DeepLearning.AI - Coursera September 2020

• Vice President and member of UIC Table Tennis A team August 2019 to May 2020

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