Customer Churn Prediction and Personalized Campaigns
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Summary
Developed a comprehensive customer churn prediction model to identify at-risk customers and inform personalized retention strategies.
About
Highly analytical Data Scientist with 2.5 years of experience at Amazon, complemented by a postgraduate degree in Data Science and Business Analytics. Proficient in Machine Learning, Python, SQL, and data visualization, with a strong foundational background in Electronics and Communication Engineering. Adept at transforming complex data into actionable business insights to drive strategic decision-making and optimize performance.
Work
Amazon India
|ML Data Associate
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Summary
Leveraged advanced data analysis and machine learning techniques to drive customer insights and optimize promotional campaign performance for Amazon Fresh India.
Highlights
Segmented over 500K Amazon Fresh India customers using RFM features and user behavior data via Python and SQL, leading to the identification of high-value segments and seasonal trends.
Developed and deployed an interactive Tableau dashboard to visualize customer segments by region, order frequency, and top product categories, enhancing strategic decision-making.
Analyzed over 50 million records of campaign, sales, and user data to evaluate promotional reach, engagement, and performance, identifying high-performing segments.
Compared key metrics including Click-Through Rate (CTR), conversion rate, and sales uplift across campaigns and product categories, providing actionable insights for optimization.
Created dynamic Tableau reports with filters to track campaign efficacy by region, time, and promotion type, enabling real-time performance monitoring.
Education
Great Lakes Institute of Management (in collaboration with University of Texas, Austin)
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Post Graduate Program
Data Science and Business Analytics
Uttar Pradesh Technical University
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B.Tech
Electronics and Communication Engineering
Skills
Machine Learning
Regression, Classification, Clustering, Time-Series Forecasting, Outlier Detection, Natural Language Processing (NLP).
ML Libraries
Scikit-learn, Pandas, Numpy, SMOTE, NLTK, spaCy, XGBoost, LightGBM.
Data Visualization Tools
Tableau, Matplotlib, Seaborn, Power BI, Plotly.
Data Analysis & EDA
Data Wrangling, Feature Engineering, Statistical Analysis, Hypothesis Testing, Exploratory Data Analysis (EDA).
Tools & Platforms
Jupyter Notebook, Git/GitHub, Google Colab, Excel (VLOOKUP, PivotTables).
Programming Languages
Python, SQL.
Projects
Regression Analysis for Airbnb House Pricing
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Summary
Conducted a detailed regression analysis to predict Airbnb house prices and identify key influential factors.
Retail Sales Time Forecasting
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Summary
Designed and implemented time-series forecasting models to predict retail sales, optimizing inventory and operational planning.