Narender Kumar

• Transformed raw data into actionable insights using advanced statistical methods, generative AI models, and Python, effectively guiding business strategy with empirical evidence.
• Optimized data-driven decision-making by applying advanced statistical techniques, Python programming, and generative AI to analyze large telecom datasets.
• Managed multiple simultaneous projects within the telecom industry, maintaining high-quality standards and meeting strict deadlines under pressure.
• Translated business requirements into data-driven solutions, leveraging Python and generative AI to provide value-added insights that directly contributed to the organization’s strategic goals in the telecom sector.

Collection Models (Predictive Modeling and Deployment with Azure)-As part of an internal tool, developed machine learning models to predict customer personas like high risk and low risk for unpaid bills. Subsequently, used MLOps (Azure DevOps and Azure services) to create a tool that enables clients to upload their customer data regularly and receive individualized predictions. These predictions empower clients to employ services like automated calls and messages for effective reminders about unpaid bills.

Credit Card Bills and Internet Service Bills Collection Optimization–

• Developed cutting-edge recommendation engine for forecasting customer issues, resulting in enhanced issue resolution and improved customer satisfaction.
• Built predictive model for credit card payment propensity, contributing to a 25% reduction in late payments and 15% increase in on-time payments.
• Transformed processing pipeline to Azure Function Apps and Container Instances, achieving impressive 80% reduction in processing time.
• Streamlined prediction process by migrating preprocessing and prediction to server, significantly enhancing overall efficiency and performance.

SKILLS – Machine Learning, Statistics, Azure DevOps, Azure, Docker, Flask

Interaction Insights of customer (NLP using generative ai(ChatGpt) and Azure)

• Created a tool capable of extracting insights from text data in six different languages. specifically focusing on chat conversations between customer service agents and customers.
• This tool categorized text data into four key categories - Agent, Process, Service, and Technology - and predicted customer loyalty, churn, journey stage, effort, and intent behind customer contacts using ChatGPT API for advanced language processing.

SKILLS – NLP(Natural Language Processing), Azure, OpenAI, Mysql

• Produced monthly reports using advanced Excel spreadsheet functions.
• Used statistical methods to analyze data and generate useful business reports.
• Improved decision-making processes with accurate data analysis and visualization techniques.
• Created various Excel documents to assist with pulling metrics data and presenting information to stakeholders for concise explanations of best placement for needed resources.
• Leveraged advanced analytical tools such as SQL, Excel, Power BI for extracting insights from large datasets quickly and accurately.
• Established long-term client relationships through consistent delivery of high-quality products and services.

Wafer Fault Detection - Machine Learning Project

Developed a Wafer Fault Detection system using Machine Learning to classify wafer sensors as faulty (-1) or functional (+1) based on sensor data.

Key Responsibilities:

• Data Validation: Automated validation of sensor data files based on schema rules (file name, column count, data types, missing values).
• Database Management: Created and managed a structured database to store validated data for model training and prediction.
• Feature Engineering: Imputed missing values using KNN imputer, removed redundant features, and applied KMeans clustering to segment data.
• Model Training & Selection: Trained Random Forest and XGBoost models for each cluster and selected the best-performing model using AUC score.
• Prediction Pipeline: Integrated pre-trained models into a Flask API for real-time wafer quality classification.

Technologies Used: Python, Pandas, NumPy, Scikit-Learn, SQL, Flask, Random Forest, XGBoost.