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Boston, MA 1(857)-991-ARYAK 7323 bodkhe.UMESH EMAIL AVAILABLE edu Linkedin GithubEDUCATIONMaster of Science in Data Analytics Engineering Northeastern University, Boston MA Sep 2022 - May 2024 Master of Business Administration, Bachelor of Technology Narsee Monjee Institute of Management Studies, India Jul 2017 - May 2022 TECHNICAL SKILLSProgramming Python (seaborn, matplotlib, NumPy, pandas), MySQL, NoSQL, R, R shiny, HTML, CSS Visualization tools Tableau, Microsoft Power BI, Data Wrapper, Flourish, Microsoft Excel, Google Sheets, Google Analytics IDE & Tools GitHub, Microsoft Outlook, Jupyter Notebook, R-Studio, AWS, Streamlit, Talend, Snowflake, Figma Core Skills Data Analysis, Data Visualization, Databases, A/B Testing, Machine Learning, Neural Networks PROFESSIONAL EXPERIENCEData Analyst Intern Ceinsys Tech LTD, India May 2021 - Sep 2021 Technology stack: Excel, Tableau, SQL Optimized sales transaction mapping by employing Excel functions like VLOOKUP, resulting 20% increase in efficient data management Delved into sales transactions of over 50 BD personnel across 5 regions using Tableau, unearthing performance insights that catalyzed a 15% increase in new market penetration Collaborated with a data team on a project focused on customer churn prediction, leading to a 30% reduction in customer churn rate and providing insights for potential revenue loss mitigation Teaching Assistant Northeastern University, Boston MA Sep 2023  Dec 2023 Evaluated assessments and led interactive lab sessions, offering targeted feedback to enhance academic performance Hosted regular office hours to provide personalized academic support, ensuring students grasp key digital manufacturing concepts ACADEMIC PROJECTSStock Price Prediction Jan 2024 - May 2024Technology stack: Python, SQL, Deep Learning Models Engineered a Deep Learning model with Bi-LSTM architecture and time series analysis for accurate predictions of future stock values Utilized Apple Inc.'s historical stock data from 2015 to 2018 to accurately predict stock prices for the year 2019 Attained an 86.8% accuracy by comparing predicted stocks with real-time data, providing valuable insights for investment decisions Brazil O-list E-commerce Data Analysis using AWS Sep 2023 - Dec 2023 Technology stack: AWS services  SQL, Glue, Lambda, Athena, S3, Quicksight Pioneered the development of an AWS cloud data pipeline tailored for Brazilian e-commerce data, utilizing Lambda functions and AWS services, reducing data processing time by 20% Achieved a 15% improvement in data integrity and reliability by optimizing storage and ETL processes with S3 and Glue, resulting in a 25% reduction in data errors Enabled interactive analysis and visualization of processed data via Athena and QuickSight dashboards, leading to a 30% increase in actionable insights derived from the dataStrategic Data-Co Global Supply Chain Analysis Mar 2023 - Apr 2023 Technology stack: Microsoft Excel, SQL, Tableau, Google sheets Developed a Tableau dashboard to optimize Data-Co Global's supply chain, with KPIs including on-time delivery rate (95%), the average time to ship (3 days), and CLTV ($2,500) Analyzed sales performance by department, market, region, and customer segment, identifying top-selling products Utilized a combination of structured and unstructured data to generate insights, including identifying late delivery risks in the Southeast region (8% of total orders) and total shipped items increasing by 15% from 2017 to 2018 Credit Card Approval Prediction Jan 2023 - Apr 2023 Technology stack: Python, Pandas, NumPy, scikit-learn, XG Boost, SVM, Logistic Regression, Random Forest, Decision Tree Conducted Exploratory Data Analysis (EDA) to unveil correlations for predicting credit card approval outcomes, achieving a correlation coefficient of 0.85 Implemented preprocessing techniques including outlier removal and SMOTE for dataset balancing, resulting in a balanced dataset with a 1:1 ratio between approved and denied credit card applications Developed and optimized an XG Boost classification model, achieving significant performance improvements with accuracy increasing from 75% to 83%, and outperforming other classification models Credit Card Approval Prediction Sep 2022 - Dec 2022 Technology stack :MySQL, Python, NumPy, Seaborn, MatPlotlib, Plotly Executed thorough data cleaning across 20 tables, mitigating inconsistencies by 98%, and drafted EER/UML relationship diagrams Implemented Employed MongoDB and SQL queries, integrating CTEs, enhancing query execution time by 40% to offer cost-effective health insuranceRESEARCH PUBLICATIONsOptical Mark Recognition with Facial Recognition System, Springer AISC, ICSCSP 2021 Feb 2021  Feb 2022