April 2020 - July 2020

Intern at Smart Bridge Techcnology

In collaboration with senior data analysts, I analyzed extensive datasets to uncover significant trends, patterns, and correlations using Numpy, Pandas, and Keras. I conducted thorough data cleaning and preprocessing with the Open Refine tool to ensure data accuracy and completeness. Additionally, I generated compelling data visualizations and comprehensive reports using the Matplotlib library, effectively communicating important insights to stakeholders.

Nov 2020 - March 2021

President at Codechef SIST Chapter

As president, you have a wide range of responsibilities. We are the visionary leader, guiding the organization's direction and encouraging people to strive toward common goals. My responsibilities include strategic planning, which entails working with colleagues to develop long-term strategies, allocate resources, and guarantee alignment with the organization's objective. My ability to make decisions has an impact on crucial issues such as policy changes and budget allocations. Aside from internal affairs, you represent the organization externally by interacting with the community, other entities, and government agencies. Effective communication, event coordination, dispute resolution, and financial management are all on your capable shoulders.

2019 -2021

Data Scientist | Accenture

Leveraged Python to analyze large-scale customer transaction datasets, identifying churn patterns and improving retention model accuracy by 30% through scalable data processing and predictive analytics. • Applied NumPy for numerical computations and large-scale data manipulation, accelerating exploratory analysis and improving processing efficiency by 20% across high-volume customer datasets. • Built churn prediction models using Scikit-learn, applying classification algorithms and validation techniques, increasing model accuracy by 28% and supporting targeted customer retention strategies. • Developed LightGBM-based models to handle large datasets efficiently, improving training speed by 35% whilecmaintaining high prediction accuracy for customer lifetime value estimation.

2024 - 2025

Data Scientist | CVS Health

• Developed predictive models using Python and Pandas, improving member engagement forecasting accuracy by 28% and enabling data-driven targeting across multiple digital health engagement channels effectively. • Built and optimized SQL queries on Snowflake, enabling efficient processing of over 5-million-member records and supporting real-time analytics, reporting, and scalable data transformation workflows. • Processed large-scale unstructured datasets using PySpark, reducing ETL runtime by 40% and enabling faster data processing to support timely insights for personalized member engagement strategies. • Engineered predictive features and trained machine learning models using XGBoost and LightGBM, improving retention prediction accuracy by 30%