469-***-**** email@example.com www.linkedin.com/in/ammrutha-shridharan EDUCATION
The University of Texas at Dallas May 2020 (Expected) M.S., Business Analytics GPA 3.52/4.00
SASTRA Deemed University June 2016
B.Tech., Computer Science and Engineering
Programming : Python, R, SAS, SQL, STATA, Java, C/C++, Ruby, MATLAB Analysis Tools : Microsoft Excel, Tableau, Power BI, R Shiny, Google Analytics Databases : mySQL, PostgreSQL, Teradata, Hive
Data Analysis : Deep Learning, Linear and Logistic Regression, Clustering, Classification, SVM, Artificial Neural Networks, Time Series Analysis, Econometrics, Conjoint Analysis, Hypothesis Testing BUSINESS EXPERIENCE
Business Analyst Intern, Fathomd Inc. (Dallas, TX) June 2019 – December 2019
• Served as a liaison between the game development and business teams of an ed-tech company funded by NSF.
• Analyzed customer data to create metrics and processes, resulting in new customer acquisition.
• Designed metrics and processes for the business development team, improving customer acquisition by 15%. Associate Business Analyst, ZoomRx Inc. (Chennai, India) January 2017 – March 2018
• Designed and delivered high impact bi-weekly reports on market insights obtained from primary market research data tracking usage of products in the liver diseases therapeutic area for a Fortune 500 life sciences company.
• Collaborated with 3+ teams on a daily basis tracking promotional effectiveness, drug awareness and patient chart audit studies for major pharmaceutical companies.
• Leveraged R Shiny to build an application to predict treatment decisions using a neural network that used historic patient chart data reported by MDs along with their behavioral patterns. Trainee Decision Scientist, Mu Sigma Inc. (Bengaluru, India) June 2016 – November 2016
• Collaborated with a US e-retail giant to develop an assortment selection algorithm to select top-selling items from 2M UPCs using decision trees using R, creating a $2M impact.
• Developed a dashboard using R Shiny that leveraged multiple data sources like Hive and Teradata to bring business KPIs together, thereby reducing the average number of data retrievals by 75%. ACADEMIC PROJECTS
Energy Usage Prediction from wireless sensor data using various ML algorithms August 2019 – December 2019
• Built and evaluated various machine learning algorithms to predict energy usage of appliances using data from a Zigbee network (Python).
Building a movie recommender system based on user preferences and ratings May 2019 – July 2019
• Developed and evaluated recommender systems using various techniques using movie 27M movie ratings from 2M users, to recommend movies to new users based on their preferences of genre (R Shiny). Reduction of Slow-moving Obsolete Inventory across leading Paint Stores Network January 2019 – May 2019
• Reduced on-hand inventory by 20% for one of the major paint manufacturers in the US by auditing the entire supply chain network to identify problem areas and provided recommendations. ACHIEVEMENTS/CERTIFICATIONS
Academic Awards : Dean’s Excellence Scholarship, The University of Texas at Dallas (2018-19) Certifications : Introduction to Computer Science and Programming using Python (MIT/edX, 2015), Data Science A-Z (Udemy, 2017), Machine Learning (Stanford/Coursera, 2019), Neural Networks and Deep Learning (deeplearning.ai/Coursera, 2019)