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Bharath KumarBK
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Bharath Kumar

@bharathkumar9

Aspiring data scientist building predictive ML, NLP, and ETL pipelines for real impact.

India
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What I'm looking for

I’m looking for a role where I can build end-to-end ML workflows—forecasting, NLP, and ETL—ship reliable pipelines, and keep improving model quality with real business feedback.

I’m an early-career data science professional who enjoys turning messy data into reliable, decision-ready models. During my Data Science Internship at Celebal Technologies, I developed predictive machine learning models with Scikit-Learn and Pandas, and engineered optimized preprocessing pipelines that reduced feature-extraction latency by 40%.

I also build end-to-end solutions—from forecasting to NLP. I architected time-series forecasting using ARIMA and Prophet, reducing historical forecast error by 15%, and I created an automated sentiment pipeline that processed 1.6M+ tweets and achieved 89% classification accuracy with TF-IDF and Logistic Regression. I lead technical community work as Coordinator of the Data Science Club, and I’m motivated by practical, explainable GenAI applications like an explainable AI framework for credit card delinquency risk.

Experience

Work history, roles, and key accomplishments

Celebal Technologies logoCT

Data Science Intern

Celebal Technologies

May 2025 - Jul 2025 (2 months)

Developed predictive machine learning models using Scikit-Learn and Pandas and engineered optimized data preprocessing pipelines that reduced feature-extraction latency by 40%. Built automated ML workflows with Matplotlib and Seaborn to streamline exploratory data analysis and save 10+ hours of manual aggregation weekly.

Education

Degrees, certifications, and relevant coursework

Bennett University logoBU

Bennett University

Bachelor of Technology (B.Tech), Computer Science & Engineering (Data Science)

2022 - 2026

Grade: 8.58 / 10.0

Pursuing a B.Tech in Computer Science & Engineering with a focus on Data Science at Bennett University (2022–2026), with a GPA of 8.58/10.0.

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