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Rajdeep Saha

@rajdeepsaha1

Data Analytics Intern at IBM SkillsBuild who built churn prediction models and interactive dashboards for business intelligence.

India

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At IBM SkillsBuild Data Analytics with AI Program, I engineered SQL and PostgreSQL queries to clean, transform, and aggregate customer, order, transaction, and product data. I also built and evaluated Logistic Regression and Random Forest models for churn prediction and risk segmentation.

I created an interactive analytics dashboard in Streamlit and worked with Power BI to present business intelligence on churn probability, revenue exposure, retention priorities, and customer behavior.

For RoleLens, I built an AI-powered platform that parses resumes against job descriptions to generate job-fit scores, skill gaps, and career recommendations. Its NLP and machine learning pipeline uses FastAPI microservices and a React.js interface.

I also developed customer intelligence and HR analytics projects, including churn prediction, Gemini API-powered analytics queries, and Power BI dashboards with DAX measures.

Experience

Work history, roles, and key accomplishments

Data Analytics Intern

IBM SkillsBuild

Aug 2026 - Sep 2026 (1 month)

Completed a 6-week intensive program focusing on end-to-end data processing, exploratory data analysis, and predictive machine learning models. Engineered SQL queries, built churn prediction models, and created interactive dashboards.

SQL PostgreSQL Python scikit learn Logistic Regression Random Forest Pandas NumPy Streamlit Power BI Data Cleaning EDA

Education

Degrees, certifications, and relevant coursework

Techno International New Town

B.Tech, Applied Electronics and Instrumentation Engineering

Pursuing a B.Tech in Applied Electronics and Instrumentation Engineering, expected to graduate in 2027.

IBM SkillsBuild

Internship Certificate, Data Analytics

2026 - 2026

Completed a 6-week intensive Data Analytics with AI internship program in association with AICTE and BharatCares, focusing on data processing, EDA, and predictive modeling.