Shruthi Kumar

Highly motivated Data Analyst with a solid foundation in Artificial Intelligence, Machine Learning, and Bachelors Degree in Electronics and communication Engineering. Proven experience in data-driven decision-making, process optimization, and statistical analysis. Skilled in Python, SQL, and Data Visualization tools like Tableau and Power BI, with a focus on deriving actionable business insights. Passionate about leveraging data to drive innovation, efficiency, and growth in dynamic organizations.

• Coursera Machine Learning Specialization: Regression, Classification, Advanced Learning Algorithms, 05/23

Recently relocated to North Carolina from the UK, with valid work authorization (H4 EAD)., Experienced in international work environments, having worked in the UK and India., Actively pursuing continuous learning and development in data science and machine learning.

Strong analytical abilities to solve complex data problems and deliver data-driven solutions., Skilled in presenting data insights and visualizations to stakeholders in a clear, concise manner., Experienced working in cross-functional teams, integrating data into business processes and decision-making.

• Credit Card Churn Prediction, Developed a logistic regression model using Python to predict customer churn, resulting in actionable insights that reduced churn by 12% for a major banking client.
• Loan Default Prediction (Hackathon Project), Improved loan default prediction accuracy using a Random Forest model. Enhanced feature selection and data preprocessing techniques to achieve a top 10% finish in a competitive hackathon.
• Personal Loan Campaign Optimization, Created a predictive model using SQL and Python to identify customers likely to purchase personal loans, leading to a 20% increase in targeted marketing efficiency.
• Customer Retention for Credit Card Services, Designed a Random Forest Decision Tree model to analyze customer behavior and increase retention rates by 8%.
• Plant Seedling Classification, Built a Convolutional Neural Network (CNN) model to classify plant seedlings into 12 categories, achieving 90% accuracy.
• Generative AI for Support Ticket Processing, Developed an AI tool to automate the classification and prioritization of support tickets, streamlining customer service operations by 25%.