Intern

Data Science Intern – Atliq Technologies, Remote
Mar 2026 – Apr 2026

• Orchestrated a data cleaning pipeline for approximately 30,000 records, resolving anomalies and improving data quality for machine learning workflows.

• Performed feature engineering by calculating ZAS and BSI metrics to identify over 9,100 high-potential competitor switchers for business analysis.

• Built and optimized an XGBoost machine learning model using Optuna Bayesian hyperparameter tuning, achieving 92% prediction accuracy.

• Designed and deployed a modular Streamlit-based “Pricing Simulator” application for real-time business scenario analysis and pricing strategy evaluation.

• Integrated MLflow and DagsHub for experiment tracking, model lifecycle management, and reproducible machine learning workflows.

• Conducted exploratory data analysis and preprocessing using Python libraries such as Pandas and NumPy to support model development and business insights generation.