Tech Stack: Python, PostgreSQL

Expert’s cases:

1. Project: Building Recommendation Engine

   • Developed a recommendation engine using chat data to identify potential new categories for the chat-bot builder

   • Utilized advanced natural language processing techniques including Bert embedding and UMAP for feature extraction and clustering using HDBSCAN for segmentation

   • Deployed the recommendation engine using the Flask framework and Docker containerization on the AWS cloud platform

   • Successfully increased customer engagement and satisfaction by providing personalized product recommendations

2. Project: Building a Chat-bot Builder

   • Integrated a chat-bot into the customer's Facebook messenger page to provide 24/7 customer service

   • Built the chat-bot using natural language processing and GPT-3 f finetuning for classification and completion API

   • Implemented machine learning algorithms to improve the chat-bot's understanding of customer queries and provide more accurate responses

   • Achieved a significant reduction in customer service inquiries and improved customer satisfaction through the implementation of the chat-bot

3. Project: Product Analytics Dashboard

   • Designed and implemented a Tableau dashboard to monitor product fitment and customer engagement on features

   • Collected and analyzed data from PostgreSQL using materialized views to provide real-time insights into customer behavior and product performance

   • Developed advanced visualization techniques to display complex data in an easily understandable format

   • Improved the decision-making process for product development and marketing by providing detailed insights into customer preferences and behavior.

4. Developed data visualization dashboards using Tableau to effectively communicate results to stakeholders, resulting in a 30% increase in stakeholder engagement.

5. Deployed the model API using the Flask Framework in Heroku, ensuring seamless integration with existing systems and resulting in a 10% increase in operational efficiency.

6. Developed a product to predict high-risk change cases in customer downtime, resulting in a reduction of customer downtime by 25%

7. Led the project from design to implementation, including the deployment on the Salesforce Einstein platform

8. Built the project using Python, resulting in improved performance and scalability

9. Generated an API which is being used by change managers to make proactive decisions, resulting in a 40% increase in change case resolution time