Ami Mayur Shah

College Park, Maryland – ***** adj5ns@r.postjobfree.com +1-240-***-**** www.linkedin.com/in/ami02/ EDUCATION

University of Maryland, College Park Dec 2020

Master of Science in Information Systems GPA - 3.77/4.0 K. J. Somaiya College of Engineering, Mumbai, India Jun 2019 Bachelor of Technology in Information Technology CGPI – 8.59/10 PROFESSIONAL EXPERIENCE

Synopsys Inc. Jun 2020 – Dec 2020

Data Analyst Intern – Software Integrity Group

• Wrote SQL queries for data extraction and analyzed customer datapoints associated with demand, sales and churn in the supply the chain pipeline - to capture spending patterns using Tableau to improve existing pricing models

• Extracted actionable insights from broad, open-ended questions to influence sales strategy and drive roadmap decisions. Created and managed reports on client data to better manage accounts

• Built costing models to determine true cost, overhead, analyze unallocated cost and profitability

• Used Tableau for data visualization of revenue analysis to model costing and revenue for personalized client targeting University of Maryland, College Park – Graduate Assistant Dec 2019 – May 2020

• Graduate Teaching Assistant for Data Analysis course in Spring 2020

• Graduate Research Assistant for large scale data extraction, sanitation, processing and storage in Fall 2019

• Research Assistantship involved writing SQL and NoSQL queries to pull and aggregate data in various formats. TECHNICAL SKILLS

• Programming languages: Python, R, Scala, SQL, Java, C, C++, HTML, CSS, React (Web Development)

• Database Technologies: MySQL, PostgreSQL, MongoDB, MySQL, Firebase, Oracle, Redis

• Big Data Frameworks & Analytics: Hadoop, Tableau, PowerBI, Apache Spark, Google Analytics, RapidMiner, Adv. Excel

• Machine Learning Frameworks: PyTorch, Tensorflow, Keras, Matplotlib, Numpy, RStudio

• Additional Skills: Amazon Web Services (AWS), Google Cloud Platform (GCP), Salesforce, ETL, Deep Learning PROJECTS

Video Games Sales Analysis May 2020 – Jun 2020

• Determined the frequency distribution of video gaming platforms using Apache Spark’s key-value RDD

• Performed big data complex join operations to analyse top gaming platform performances in various regions

• Performed growth forecast by finding total sales evaluations associated with each gaming genre and their platforms Airbnb Austin Market Analysis Apr 2020 – May 2020

• Created data pipelines using Python and performed data profiling using R programming to predict real estate profitability for investors

• Designed Bagged Trees and KNN models and performed cross validation using Python and Tensorflow

• Implemented Ridge and Lasso regression to assess and mitigate multicollinearity and to perform variable selection

• Produced reports with inferences using standard statistical tests to answer research questions Topic Modelling - Obama Trump Tweets Oct 2019 – Dec 2019

• Extracted a live stream of Obama-Trump tweets using Python and created a data pipeline for sanitation, processing and storage of tweets for topic modelling and exploratory analysis

• Developed deep learning model to analyze topic sentiments to find contrasts between the two tweet categories

• Implemented data visualization using Tableau to create reports about public opinions, behavior and attitude based on individual models

Movie Recommendation Engine Jun 2018 – Jul 2018

• Developed 2 recommendation systems using Python and Keras framework and trained them on a benchmark MovieLens dataset containing 1 million records

• Created and tested a Content-Based Model on movie genres and Collaborative Filtering System on user ratings with high accuracy.

• Improved model performance using sparse matrix factorization and ensemble deep learning models Semantic Modelling for Analysing Sentiment Mar 2018 – Apr 2018

• Designed a data pipeline in Python to perform data sanitation and process tweets to predict sentiment inclination using Random Forests and SVMs

• Built and deployed multilayer perceptron network and 1-D CNN to capture semantic structure

• Improved representation of target groups by more than 20% by utilizing trained word embeddings

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