SIDDHI SANDBHOR

Senior Research Associate with two years of experience at Grand View Research, Inc. Led market research initiatives that enhanced client decision-making and retention. Created data visualizations and conducted thorough competitive analyses, yielding strategic insights and impactful presentations. Proficient in Python, SQL, Tableau, and comprehensive analytics workflows for generating actionable insights.

Certificate - IFACET IITK.pdf

1.1. Comparison of regions based on sales

• Developed an interactive Tableau dashboard using the Sample Superstore dataset, enabling comparative analysis between two regions through dynamic parameters (Primary Region and Secondary Region) and calculated fields for metrics like Total Sales, Avg Sales per Order, and Total Customers and Orders
• Configured hierarchies across country, state, and city, and implemented first order date logic to segment regional performance over time, enhancing drill-down capability and regional insights
• Designed a well-organized multi-sheet dashboard layout, including bar charts, maps, and tables to display cross-region comparisons, facilitating management decision-making by visualizing trends and performance gaps between regions

1.2. British Airways review

• Built a complete end-to-end Tableau dashboard using consumer review data (British Airways) sourced from CSV, incorporating data cleaning and data blending, with a Countries.csv lookup for geographic context
• Developed interactive visualizations exploring overall satisfaction ratings, cabin service, food, entertainment, and more by aircraft type, seat class, traveler type, country, and continent, enabling drill-down and filter capabilities over time (2016–2023)
• Designed a polished multi-sheet Tableau dashboard with global maps, trend analyses, filter controls, and dynamic segmentation to support stakeholder insights into customer experience across all dimensions

1.3. Apparel sales analysis project

• Cleaned and prepared Q4 sales data using Pandas, resolving missing data issues, and standardizing variables; conducted exploratory data analysis (EDA) to evaluate state and segment level performance
• Generated time-series reports (daily to quarterly) using Pandas resampling, and conducted segmented analysis by demographic and geographic variables, including time-of-day insights
• Developed interactive data visualizations with Seaborn and Matplotlib, and documented insights and recommendations in a Jupyter Notebook for data-driven decision-making

1.4. ScienceQtech employee performance mapping

• Designed and implemented a MySQL relational database from CSV files with employee records, project details, and team structure, developed ER diagrams, tables, and data pipelines for efficient query operations
• Created and optimized advanced SQL queries, views, stored procedures, and user-defined functions for performance segmentation, experience-based job role mapping, salary analytics, and bonus computation
• Applied window functions, aggregation, joins, and indexing strategies to rank employees, track manager reports, and improve query performance for HR and BI use cases

1.5. Healthcare insurance analysis

• Cleaned and integrated multi-source healthcare datasets using Pandas and SQL, engineered key features like age and gender (via name parsing), and created dummy variables for selective state IDs and categorical tiers to improve model relevance
• Performed EDA and statistical tests (ANOVA, chi-square) to evaluate hospitalization costs across hospital tiers, city types, and lifestyle variables; visualized insights using Matplotlib, Seaborn, and Tableau dashboards for stakeholder storytelling
• Built and validated regression models (linear, ridge, gradient boosting) using scikit-learn pipelines, stratified k-fold CV, standardization, and feature importance analysis; visualized outcomes via Tableau dashboards