Rhea Mathur

Dedicated and passionate job seeker with strong organizational skills, actively pursuing an entry-level Data Analyst or Software Engineering Intern position. Equipped with exceptional communication and project management abilities, demonstrating a detail-oriented approach to tasks while effectively managing multiple responsibilities in fast-paced environments. Proven expertise in data visualization, statistical analysis, and database management, complemented by proficiency in Python, SQL, and Excel. Recognized for a proactive mindset in identifying challenges and driving actionable insights, committed to supporting team objectives and contributing to overall company success.

Shieldra – Autonomous Threat Detection System | Python, Machine Learning, Streamlit
• Designed and developed an autonomous threat detection system that leverages machine learning and data analysis techniques to identify potential risks. The system incorporates an agentic AI loop enabling continuous monitoring, adaptive learning, and real-time decision-making. An interactive dashboard was built using Streamlit to visualize threat patterns and system responses, while data preprocessing, anomaly detection, and predictive modeling techniques were applied to ensure accurate and scalable threat identification
Employee Attrition Prediction | Python, Pandas, Scikit-learn
• Developed a machine learning classification model to predict employee attrition using HR analytics data. The project involved comprehensive exploratory data analysis (EDA) to identify key factors influencing employee turnover, followed by feature engineering to improve model performance. Techniques such as SMOTE were applied to handle class imbalance, and multiple models were evaluated using cross-validation to ensure robustness. The results were supported with data visualizations to derive actionable insights for workforce retention strategies.
Netflix Data Analysis (EDA Project) | Python, Pandas, Seaborn, Matplotlib
• Performed exploratory data analysis on a real-world Netflix dataset to uncover trends in content distribution and viewer preferences. The analysis included examining genre patterns, release trends, and regional availability of content. Data cleaning and preprocessing were carried out to ensure data quality, followed by the creation of insightful visualizations using Seaborn and Matplotlib. The project demonstrates strong data storytelling and the ability to extract meaningful insights from large datasets.

• Participated in Google Cloud Study Jams (completed 19+ skill badges and hands-on labs).
• Actively working on real-world data science projects and publishing them on GitHub.
• Currently exploring advanced machine learning concepts and feature engineering techniques.
• Exploring visualization dashboards like Tableau and PowerBI

- Currently building end-to-end machine learning systems with focus on automation and real-time decision-making.
- Exploring agentic AI systems and their applications in autonomous environments.
- Continuously learning through platforms like Kaggle and Coursera.
- Strong interest in pursuing research and higher studies in Data Science and Machine Learning.