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Click here or scroll down to respond to this candidate Candidate's Name
Parkville, MD|PHONE NUMBER AVAILABLEEMAIL AVAILABLE|Linkedin|Github
EDUCATION
Master of Science in Data Analytics Engineering |Northeastern University, Boston MA Sep Street Address - May 2024
Master of Business Administration, Bachelor of Technology |Narsee Monjee Institute of Management Studies, India Jul Street Address - May 2022
TECHNICAL SKILLS
Programming 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 EXPERIENCE
Data Analyst Intern | Ceinsys Tech LTD, India May 2021 - Sep 2021
Technology stack: Microsoft 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 PROJECTS
Business Insights 360 - Data Analytics and Visualization in Business[Link] Jan 2024 - May 2024
Technology stack: SQL, Power BI
Engineered a Power BI dashboard integrating financial, sales, supply chain, marketing, and executive data, resulting in a 15% increase in
overall revenue by identifying key growth opportunities
Analyzed revenue, expenses, and profits to refine performance assessment, leading to a 20% reduction in decision-making time and
utilized sales data to develop targeted sales strategies, gain customer insights, and drive a 10% increase in sales
Brazil O-list E-commerce Data Analysis using AWS [Link] 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 data
Strategic Data-Co Global Supply Chain Analysis [Link] 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) using Python libraries 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
Health Insurance Database System 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
Employed MongoDB and SQL queries, integrating CTEs, enhancing query execution time by 40% to offer cost-effective health insurance
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