SUPRIYA MOVVA

*** **** **, ********, ***********, CT 06106

adjjmg@r.postjobfree.com +1-908-***-**** https://www.linkedin.com/in/supriyamovva/ SUMMARY

Current Graduate Student with Analytics background and more than 2 years of experience in Data Science and Machine Learning algorithms. Skilled in utilizing statistical / predictive models and cutting-edge algorithms utilizing diverse sources of data to predict demand. Successful in implementing data-driven strategies that fuel growth including revenue and profits. EDUCATION

University of Connecticut School of Business, Hartford, CT Master’s of Science, Business Analytics and Project Management, Aug 2020-Dec 2021. GPA: 4.2/4.3 Amrita Vishwa Vidyapeetham, Kerala, India

Bachelor’s of Technology, Computer Science, 2014 – 2018. GPA: 9.4/10 TECHNICAL SKILLS

Analytics:Regression, Classification, Decision Trees, Random Forest, Naive Bayes, Ensemble Models, Neural Networks, K-Means Clustering, PCA, Predictive Modelling, Time Series Forecasting, Hypothesis Testing(t-test, z-test, chi-squared, ANOVA). Tools & Programming:R, Python, Pandas, Pyspark, Jupyter Notebook, JMP Software, SQL (Oracle, PostgreSQL), Hadoop, Microsoft Excel, Pivot Tables, Java, PowerPoint, AWS, MS PowerPoint, Visio,SciKitLearn. Business Intelligence and Data Visualization: R shiny, Excel VBA, Apache Superset & SQL Databases, ggplot2, Tableau, Power BI. WORK EXPERIENCE

Data Analyst, Future Group, India (July 2019 – Aug 2020) Customer Level Data Management:

• Managed the data of Fifty Million household’s transactions in AWS S3 using Pyspark and SQL technologies

• Aggregated tables at a member level using customer transactions and promotions data in Apache Superset database

• Automated and Scheduled Pyspark jobs at regular intervals using Crontab tool saving 4 work hours every week Supporting Insurance Team with customer data:

• Delivered Customer Cohorts for specific product categories to improve the sales and profitability of the category

• Built interactive Dashboards using key performance indicators for business to understand the insights better

• Collaborated with insurance team to generate member attributes to gain business understanding of the data Decision Scientist, Mu Sigma Business Solutions, India (June 2018– July 2019) Market Share Forecasting:

• Predicted the Market Share of various categories across 66 Markets using key drivers that influence Market Share for a Leading Snack Manufacturing company in US

• Implemented Feature Selection techniques like Boruta and Stepwise Linear Regression along with Time-series forecasting models like Arimax and Prophet to predict Market Share

• Developed an Interactive Scenario Planner using R Shiny which allows user to create, compare and analyse the various scenarios by forecasting the Market Share in real time Impact: Improved performance of the prediction with 95% accuracy and reduced uncertainty with anticipated changes Trade Promotion Forecasting:

• Forecasted the sales volume and identified the drivers impacting the sales volume generated from promotions for one of a Leading Snack Manufacturing Company in US

• Developed multi-model approach to arrive at the best fit for each combination using Elastic Net, MARS, GAM along with feature selection techniques such as Boruta, Lasso and Stepwise Linear Regression Impact: Model helped in understanding the demand to better manage the inventory Household Data Analysis:

• Collaborated with the Marketing Insights team of a Leading Snacks Manufacturing company in US

• Analyzed the household data and leveraged it to help business enable rich data driven decisions

• Generated insights on how consumers perceive brand value and fluctuations in consumption at different price points Impact: Achieved the target in customizing the product portfolio with efficient price points ACHIEVEMENTS

• Published a research paper in Springer on “Prefix Tree based Map Reduce Approach for Mining Frequent Subgraphs”

(https://link.springer.com/chapter/10.1007/978-3-030-20615-4_17)

• Won Spot Award for improving the accuracy of the prediction in Market Share Forecasting project