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Vegesna Srilekha

Data Analyst • Visakhapatnam, India • s**************@gmail.com • +91*******426 • •••••

Professional Summary

Results-oriented Data Analyst with internship experience in SQL, Python, Power BI, Advanced Excel and Tableau. Experienced in ETL, data cleaning, data modeling, dashboard development (DAX, Power Query), and statistical analysis. Proven ability to turn 50,000+ record datasets into actionable BI dashboards and operational improvements (reduced manual reporting effort by 40%, cut supply lead times by 15%, improved delivery accuracy by 20%). Seeking an entry-level Data Analyst role to apply analytics and visualization skills to drive business insights and measurable outcomes.

Technical Skills

Programming Languages: Python,VBA
Frameworks and Libraries: Pandas,NumPy,Matplotlib,scikit-learn
Databases: SQL,MySQL,PostgreSQL,Joins,Common Table Expressions,Window Functions,Stored Procedures
Data and Analytics: Power BI,DAX,Power Query,Tableau,Data Visualization,Excel,Star-schema design,Data Modeling,ETL,Data Cleaning,Data Validation,Statistical Analysis,RFM Analysis,K-Means Clustering,KPI Reporting,Business Intelligence,Data Governance,Data-driven Decision Making
Tools and Methodologies: Jupyter Notebooks,Git
Spreadsheet & Automation: Pivot Tables,Power Pivot,Macros,XLOOKUP,Power Automate

Work Experience

Vijayasri Organics
Visakhapatnam, India
Data Analyst Intern
Sep 2024 – Feb 2025
Tech Stack: Power BI, DAX, Power Query, SQL, Python, Excel, Power Automate
  • Designed and deployed interactive Power BI dashboards (Power BI, DAX, Power Query) from a 50,000+ record sales and operations dataset to monitor inventory, sales, dispatch and delivery KPIs for daily operational tracking.
  • Analyzed 50,000+ records using SQL and Advanced Excel to identify demand and fulfillment bottlenecks; recommended supply chain changes that reduced lead time by 15% and improved delivery accuracy by 20%.
  • Automated recurring business reports and scheduled refreshes using Power Automate and Power BI, reducing manual reporting effort by 40% and improving timeliness of insights.
  • Performed end-to-end data cleaning, validation and transformation with SQL, Python (Pandas) and Excel to improve data quality and reporting accuracy across dashboards.
  • Implemented star-schema data modeling and optimized Power BI data model to improve query performance and maintainability of reports for cross-functional teams.
  • Collaborated with business stakeholders to translate requirements into measurable KPIs and interactive visualizations, enabling stakeholders to make data-driven decisions and prioritize operational improvements.
KPMG (Forage)
Data Analytics Virtual Intern
Tech Stack: Python, Pandas, scikit-learn, Matplotlib, Power BI, Excel
  • Conducted data quality assessments to identify inconsistencies and missing values, documenting issues and proposing remediation steps to improve dataset reliability.
  • Cleaned and transformed datasets using Python (Pandas) and Excel, applying validation rules to produce analysis-ready tables and reduce downstream errors.
  • Performed customer segmentation using RFM analysis and K-Means clustering with scikit-learn, deriving four distinct customer segments and actionable targeting recommendations.
  • Created visualizations and business reports using Matplotlib and Power BI to communicate segment characteristics, revenue distribution and retention opportunities.
  • Prepared and presented a consulting-style case study summarizing analytical findings and strategic recommendations to stakeholders.
  • Documented methodology, assumptions and reproducible code artifacts to ensure clear handover and reproducibility of analyses.
Analytics

Projects

Sales Performance Dashboard
Tools Used: Power BI, SQL, Excel, DAX, Star-schema design
  • Designed an interactive Power BI sales dashboard with 12+ KPIs using 50,000+ sales records to monitor revenue, conversion and product performance.
  • Developed DAX measures and calculated columns and built a star-schema data model to enable efficient slicing, filtering and automated refreshes that supported sales performance analysis.
Customer Segmentation Analysis
Tools Used: Python, SQL, scikit-learn, Matplotlib, RFM Analysis
  • Cleaned and analyzed customer transaction data using Python and SQL; applied RFM analysis and K-Means clustering to segment customers into four groups based on value and engagement.
  • Visualized segment behavior with Matplotlib and produced actionable recommendations for targeted marketing and retention strategies.
HR Analytics Dashboard
Tools Used: Power BI, Excel, SQL, Data Visualization
  • Built HR dashboards to monitor attrition, attendance and workforce KPIs; analyzed demographic and performance trends to surface retention risks.
  • Implemented interactive filters and drill-through reports to enable HR managers to explore drivers of turnover and prioritize interventions.
COVID-19 Global Data Analysis
Tools Used: Python, Pandas, Matplotlib, Statistical Analysis
  • Processed and analyzed 100,000+ COVID-19 records performing exploratory data analysis to identify trends in cases, recoveries and mortality across countries.
  • Built comparative visualizations and produced a statistical summary report that highlighted key temporal and geographic patterns.

Education

Miracle Educational Institutions and Society
MBA • 2025

Certifications

Google Professional Data Engineer — Google
Cloudera Data Analyst — Cloudera
IBM Data Analysis with Python — IBM
Introduction to SQL — Coursera

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