Anitha Morampudi

Arlington,TX

Overview

years of professional experience

Certification

Mahindra & Mahindra

01.2022 - 12.2023

Led data-driven projects focused on ECU software performance, utilizing machine learning techniques (clustering, regression) to predict software defects under varying testing conditions
Collaborated with cross functional teams using Agile methodologies, enhancing product development cycles and improving delivery timelines
Conducted advanced data analysis using SQL and Python to optimize software testing efficiency, identify process inefficiencies, and improve defect detection rates
Developed interactive Tableau dashboards for reports presenting key insights on software performance and defect trends to senior leadership
Contributed to strategic product decisions by analyzing market trends and regulatory changes in the automotive industry, supporting data-driven business growth

Cognizant Technologies Solutions

08.2021 - 12.2021

Applied advanced data science techniques to analyze clinical trial data for cancer treatments, enabling faster and more accurate drug dosage identification tailored to patient profiles
Developed an automated text-mining pipeline to review over 10,000 resources, normalize data, and deliver actionable insights, reducing the review process from 20 months to 20 days
Streamlined oncology research by optimizing data workflows, shortening drug development timelines by up to four years, and reducing clinical trial costs by 8-10%
Created scalable solutions for cross-referencing research data, paving the way for application to other treatments, including Alzheimer's, depression, and schizophrenia

University of Texas At Arlington

Arlington, TX

12.2025

Shri Vishnu Engineering College for Women

Bhimavaram, India

05.2022

Image Classification Using CIFAR-10 | Python, TensorFlow

Developed and trained a CNN model using TensorFlow achieving 92.5% accuracy on the CIFAR-10 test dataset with 10 distinct image classes.
Processed 60,000 images using data normalization and augmentation, leading to a 15% improvement in model generalization.
Reduced overfitting by implementing dropout layers (30%), batch normalization, and hyperparameter tuning, ensuring stable model convergence.
Evaluated performance using confusion matrices and precision-recall analysis, achieving 91% precision and 89% recall across all classes.

Dallas Police Response Time Analysis | Python, Matplotlib, Tableau

Processed 180,000+ police incident records (2014–2019) using Python (Pandas, NumPy) to analyze response times based on location, time of day, and incident type.
Engineered a custom response time metric and created interactive visualizations with Tableau and Matplotlib to identify factors affecting police arrival times.

Company Bankruptcy Prediction | Python, Tableau