Bhawna Pal

Data Analyst

Delhi NCR, India 0+ yrs exp 81 · Excellent

About

Computer Science graduate (B.Tech, 2026) with hands-on experience in SQL, Python (Pandas, NumPy, Scikit-learn), data cleaning, feature engineering and dashboard reporting, gained through a machine learning internship and analytics-driven projects. Skilled at turning raw data into clear insights, validating data quality, and presenting findings through interactive dashboards. Backed by a software engineering background in Java, Spring Boot and MySQL, with experience building data pipelines and REST APIs. Seeking an entry-level Data Analyst role.

Skills & Expertise (38)

Python Advanced

7.8/10

Years Exp

Data Cleaning Advanced

7.0/10

Years Exp

Data Preprocessing Advanced

7.0/10

Years Exp

Exploratory data analysis Advanced

7.0/10

Years Exp

Feature Engineering Advanced

7.0/10

Years Exp

scikit-learn Advanced

7.0/10

Years Exp

Matplotlib Advanced

7.0/10

Years Exp

Seaborn Advanced

7.0/10

Years Exp

SQL Intermediate

6.5/10

Years Exp

GitHub Intermediate

6.0/10

Years Exp

Git Intermediate

6.0/10

Years Exp

AWS services Intermediate

5.5/10

JavaScript Intermediate

4.5/10

Java Intermediate

4.5/10

REST APIs Intermediate

4.5/10

Docker Intermediate

4.0/10

FastAPI Intermediate

4.0/10

Hugging Face Spring JDBC Gradle Postman IntelliJ IDEA VS Code Agile Scrum LLMs LangGraph LangChain JWT Authentication Spring Boot Streamlit Jupyter MATLAB NumPy Pandas Reporting Data Validation MySql

Work Experience

Junior Engineer Intern

Ironbook AI

Jun 2025 - Sep 2025

Developed INitro, a multi-agent decisioning system for customer segmentation and campaign generation, enabling autonomous decision-making and task execution. Built LLM agent workflows with LangChain and LangGraph, improving system efficiency and real-time response. Integrated APIs and optimized data pipelines and prompt design, improving accuracy, scalability and overall performance.

Machine Learning Intern

Smart Swift Innovation Pvt. Ltd.

Aug 2024 - Sep 2024

Cleaned, preprocessed and explored real-world datasets using Python (Pandas, NumPy), handling missing values, outliers and feature engineering to prepare data for analysis. Analysed patterns and trends and presented findings through visualizations built with Matplotlib and Seaborn. Built and evaluated supervised and unsupervised models with Scikit-learn, improving performance through hyperparameter tuning and iterative evaluation of metrics.