V

Vineeth Gadila

About

Detail

District of Columbia, United States

Timeline

work
Job
school
Education

Résumé

Jobs verified_user 0% verified
  • Inland Empire Health Plan
    Data Analyst
    Inland Empire Health Plan
    Jan 2025 - Current (1 year 6 months)
    • Automated reporting pipelines with Python and Excel, reducing manual work by 40%. • Designed Tableau dashboards to monitor customer KPIs, increasing engagement by 15%. • Optimized SQL queries, improving reporting efficiency by 30%. • Work with leadership and department heads to provide data analysis for strategic planning. • Collaborated with cross-functional teams to deliver insights supporting key business strategies. • Developed ETL workflows to integrate data from multiple sources, improving accuracy and availability for real-time decision-making. • Performed in-depth trend and variance analysis to identify performance gaps and support forecasting initiatives. • Presented analytical findings through clear visualizations and re
  • Kent State University
    Research Data Analyst
    Kent State University
    Sep 2023 - Dec 2024 (1 year 4 months)
    • Developed Power BI dashboards analyzing learning management system data for 5,000+ users. • Analyzed 500,000+ student records using SQL and Python, maintaining 99% data accuracy. • Built automated data validation processes to catch errors before impacting reports. • Presented analysis findings to university leadership every two weeks. • Worked with academic departments to improve data collection and management practices. • Contributed to initiatives that improved student retention by 12%. • Designed predictive models to identify at-risk students early, enabling targeted interventions that boosted retention outcomes. • Streamlined reporting workflows by automating routine queries, reducing manual processing time by 35%. • Translat
  • I
    Junior Data Analyst
    Inventzo Systems
    Dec 2021 - Aug 2023 (1 year 9 months)
    • Developed financial and operational dashboards in Power BI and Excel for business stakeholders. • Supported predictive modelling by preparing and validating datasets. • Implemented quality checks that reduced reporting errors by 15%. • Automated parts of the monthly financial close process, saving 5 days per cycle. • Supported various analytics initiatives across finance and operations teams. • Optimized SQL queries and Excel models to improve data refresh speed and reporting efficiency by 25%. • Identified cost-saving opportunities through variance analysis and data-driven forecasting. • Collaborated with FP&A teams to design KPI reports, enabling more accurate budgeting and resource allocation. • Developed automated reporting t
Education verified_user 0% verified
  • Kent State University
    Master of Science
    Kent State University
    Aug 2023 - May 2025 (1 year 10 months)
  • T
    Tableau Desktop Specialist
  • I
    ISTQB – Foundation Level (CTFL)
  • M
    Microsoft Power BI Data Analyst Associate
  • A
    AWS certified Data Analytics – speciality
  • G
    Google Data Analytics Professional Certificate
Projects (professional or personal) verified_user 0% verified
  • F
    Finance KPI Automation
    Automated a manual reporting process that took 40 hours each month. Built a Python-based pipeline that pulls data from 5 different sources, transforms it, and generates reports in 10 hours with no manual intervention. Eliminated data entry errors and improved consistency across reports. I implemented data validation checks to ensure accuracy at every stage of the pipeline and set up scheduled runs to deliver reports on time without delays. I also created centralized documentation and reusable scripts to make the process easy to maintain and scale. The automation reduced dependency on manual work, improved reliability, and freed up the team's time to focus on analysis instead of repetitive reporting tasks.
  • S
    Sales Forecast Dashboard
    Built a machine learning model to forecast sales across 20+ product categories using 3 years of historical data. The model improved forecast accuracy by 20%, enabling the business to optimize inventory levels and reduce carrying costs by approximately $ 300,000 annually. Created an interactive dashboard for exploring forecasts at different levels.
  • C
    Customer Churn Analysis
    Developed a classification model that identifies customers at risk of churning with 85% accuracy. Built a Power BI dashboard showing risk scores and key factors driving churn. The insights helped the customer success team reduce churn by 10%, retaining around $1.2M in annual revenue. collected and cleaned customer usage data from multiple sources to ensure data quality before model training and analyzed behavioral patterns to identify the most influential predictors of churn. I also partnered closely with the customer success team to translate the model output into clear, actionable strategies, allowing them to prioritize outreach efforts and improve retention outcomes.