AJIT VALKERI

Data Analyst with 3.4 years of experience in transforming raw data into actionable insights. Skilled in Python, SQL, Power BI, Tableau, and AWS, with strong expertise in data cleaning, statistical analysis, predictive modelling, and visualization. Experienced in building scalable ETL pipelines, developing recommendation systems, and delivering AI-powered sentiment analysis solutions.

E-commerce Product Recommendation Engine, Client: NextCart Solutions, 

Role: Data Analyst, Duration: 03/09/23 to 02/10/25, Domain: E-Commerce

Tech Stack: Python, Pandas, SQL, Power BI, AWS, Statistical Models.

Developed a personalized product recommendation engine to enhance customer engagement and sales. The solution combined collaborative filtering, content-based algorithms, and statistical models to suggest products based on browsing and purchase history. Integrated real-time recommendation APIs with the e-commerce platform and built dashboards to monitor customer behaviour, enabling smarter inventory and marketing decisions., Analysed customer purchase and browsing data using clustering and association rule mining., Designed and implemented collaborative filtering and content-based filtering algorithms., Built a real-time recommendation API for dynamic suggestions on the e-commerce platform., Created dashboards to track sales trends and customer engagement. 

Key Contributions:

• Analysed customer purchase and browsing data using clustering and association rule mining.
• Designed and implemented collaborative filtering and content-based filtering algorithms.
• Built a real-time recommendation API for dynamic suggestions on the e-commerce platform.
• Created dashboards to track sales trends and customer engagement.

AI-Powered Sentiment Analysis Social Media, Client: LearnVista EdTech Pvt. Ltd,

Role: Data Analyst, Duration: 01/06/22 to 01/09/23 Domain: Marketing

Tech Stack: Python, Excel, SQL, Pandas, Azure.

Led the design of an AI-powered sentiment analysis solution to track and analyse public perception of EdTech services across YouTube and Facebook. Built NLP-based models to classify customer comments, correlated sentiment insights with ongoing campaigns, and visualized results in interactive dashboards. The solution enabled management to track brand perception, optimize marketing spend, and improve student engagement strategies., Collected and processed large volumes of social media data using APIs., Built NLP-based sentiment models leveraging Python, Pandas, and Numpy., Designed Power BI dashboards visualizing sentiment trends and campaign impact., Developed predictive models for demand forecasting and operational planning.

Key Contributions:

• Collected and processed large volumes of social media data using APIs.
• Built NLP-based sentiment models leveraging Python, Pandas, and Numpy.
• Designed Power BI dashboards visualizing sentiment trends and campaign impact.
• Developed predictive models for demand forecasting and operational planning.