Govind Kumar Gupta

Viewership Metrics Analysis: Conducted in-depth analysis of key viewership metrics (NGRP, target population), generating regular reports for stakeholders to inform decision-making processes.

Process Optimization :- Designed algorithms, leveraging python and statistics for Benchmarking and other reports, resulted in 60% improved performance and accuracy. Hence, doubled the team's productivity.

Cross-Functional Communication:- Collaborated with Data Engineer's team to build 2+ UI for the generation of the reports which fastend the analytics process.

Data Artitecture:- Designed a data warehouse which improved data accessiblity across teams and contributed in providing real-time insights through interactive dashboard.

Leadership:- Mentored junior and fellow analysts to learn skills like Python, SQL, which led to a increase in 20% team's productivity.

Latest news Updates:- Developed a web scraper using Selenium to extract relevant news headlines from leading industry websites such as IndiaTelevision, e4m, MediaNews4U, and BestMediaInfo.Reduced the time needed to analyze data and increased accuracy, which tripled the team's efficiency.

Key Metrics Optimization:- Modified logic to calculate buying prices throughout the supply chain and analyzed trade margin metrics and other key performance indicators (KPIs) using Tableau.

Cross-Team Collaboration:- Collaborated with different stakeholders to understand functionality and requirements, hence build 50+ interactive Tableau dashboard.

ETL Development:- Created an ETL layer on top of database using PostgreSQL and Python, defining the schema structure for the Data Warehouse to ensure efficient data processing and management.

• Build the postgres SQL query repository that act as inbuild functions and interact with the metadata to shows up the required results
• Set up the chain to automatically analysis the data through google Data Studio and build dashboard to obtained the useful insights

• Researched on the Firebase data (7 lakh+ entries) from API, determined the variation between the booking and response time of app
• Forecasted the ticket's booking data for the next 2 month using RFE and auto regression model thus obtained the accuracy of 71%