NANDHINI P

Data Scientist with expertise in Python, predictive modeling, and ethical AI practices. Proven ability to extract actionable insights from complex data sets using tools such as scikit-learn and pandas. Skilled in creating clear visualizations and strategic recommendations that drive business decisions. Committed to leveraging data preprocessing and fairness metrics to foster responsible innovation and measurable impact.

English, Tamil, Hindi(Beginner)

• 3/23 A west Street, Pappankulam
• 8940950092
• Kalyaninandhini5@gmail.com
• 14/08/2005

TATA

May 2025 - June 2025

GenAI Powered Data Analytics

Tata Group data analytics job simulation on Forage, June 2025

• Conducted exploratory data analysis (EDA) using GenAI tools to assess data quality, identify risk indicators, and structure insights for predictive modeling

• Proposed and justified an initial no-code predictive modeling framework to assess customer delinquency risk, leveraging GenAI for structured model logic and evaluation criteria.

DELOITTE

Jun 2025 - Jun 2025

Data Analytics

Deloitte Australia Data Analytics Job Simulation on Forage - June 2025

Completed a Deloitte job simulation involving data analysis and forensic technology

• Created a data dashboard using Tableau

Used Excel to classify data and draw business conclusions.

 Customer Churn Analysis
Analyze telecom or SaaS customer data to predict churn. Highlight your preprocessing skills—handling missing values, outliers, and feature engineering—and visualize insights using Tableau or Power BI. A fairness-aware resume screening tool using Python, NLP, SHAP, AIF360, and Scikit-learn was developed as an AI-powered resume screening prototype that evaluates candidate profiles using natural language processing while mitigating algorithmic bias. Fairness metrics were implemented using AIF360 to detect and reduce disparate impact across gender and ethnicity, and integrated SHAP for model explainability, ensuring transparent decision-making Achieved a balance between predictive accuracy and ethical compliance, aligning with responsible AI principles.

Fairness-aware resume screening tool,

Python, NLP, SHAP, AIF360, and Scikit-learn developed an AI-powered resume screening prototype that evaluates candidate profiles using natural language processing while mitigating algorithmic bias, implemented fairness metrics using AIF360 to detect and reduce disparate impact across gender and ethnicity, and integrated SHAP for model explainability, ensuring transparent decision-making Achieved a balance between predictive accuracy and ethical compliance, aligning with responsible AI principles.