About
Highly analytical and results-driven Applied Data Scientist with expertise in retail and e-commerce sectors, leveraging SQL, Python, and Tableau to translate complex data into strategic, actionable insights. Proven track record in enhancing business performance through customer insights, churn reduction, campaign optimization, and conversion funnel analysis. Eager to apply advanced data science methodologies to drive impactful, measurable results for leading organizations in the retail industry.
Work
New York, NY, US
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Summary
Spearheaded consumer insights initiatives, leveraging advanced analytics and machine learning to optimize marketing strategies and enhance customer experience for a global audio brand.
Highlights
Extracted, processed, and cleansed over 100,000 customer records, performing extensive exploratory data analysis (EDA) to uncover meaningful patterns and trends that guided data-driven marketing decisions.
Developed and implemented a continuous sentiment analysis model using Python and NLTK tools (e.g., TextBlob), achieving 88.5% accuracy in classifying customer feedback for product development.
Designed and executed comprehensive customer segmentation using Seaborn and Matplotlib, effectively identifying distinct customer attitudes and behaviors to inform targeted marketing strategies.
Conducted a detailed SWOT analysis and competitor assessment, developing actionable strategies for market improvement and identifying new opportunities for growth.
Melbourne, Victoria, Australia
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Summary
Drove significant improvements in retail and e-commerce operations by applying advanced data analytics techniques to solve real-world business problems.
Highlights
Completed a structured curriculum covering SQL, Tableau, and Python, applying these skills to solve real-world problems in retail and e-commerce domains, enhancing data-driven decision-making.
Managed and cleansed large customer datasets with SQL, ensuring high data quality and revealing critical insights into customer behavior across the e-commerce platform.
Reduced customer churn by 25% within six months by identifying and addressing critical factors in bank customer data through advanced segmentation and predictive analysis.
Built an interactive Tableau dashboard to monitor key churn metrics and trends, supporting early identification of at-risk customers and targeted retention strategies.
Projects
Retail Store Performance Analytics and Campaign Optimization
Summary
Analyzed 2.5M household-level transactions covering 583 grocery stores over two years and uncovered that 12% of stores generated 80% of total sales value. Developed and applied an RFM segmentation model to classify customers and identified the best customer segment with high purchasing frequency and monetary value comprising 20.24% of total customers. Evaluated the performance of 30 marketing campaigns and revealed Campaign 18 as the most effective with a 486.6% ROI compared to other campaigns. Recommended targeting the best customer segment with campaign 18 and predicted a 12.06% sales growthacross underperforming stores over 60 days.
Skills
Programming & Databases
SQL, Python, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, Google BigQuery.
Data Visualization & BI
Tableau, Looker Studio, Data Visualization, KPI Reporting.
Data Science & Analytics
Data Cleaning, ML/DL Development, Customer Insights, Marketing Campaign Optimization, Conversion Optimization, Sentiment Analysis, Customer Segmentation, Predictive Analysis, Exploratory Data Analysis (EDA).