Hi, I'm Govardhan Reddy Baddala.

A Data Science Graduate Student

From thoughtful software builds to hands-on ML experiments and practical data insights, I focus on work that helps people ship faster. I solve problems by writing reliable code, training ML systems that behave, and turning raw data into plain-language narratives.

About Me

I'm a Data Science graduate student at California State University, Chico graduating May 2026 with 2.5+ years of experience across machine learning, cloud optimization and full stack work. Whether it's building software that scales or automating analysis I like solving hands on problems and getting products into people's hands.

Recently I've shipped AI assistants, interactive dashboards, cloud cost monitoring tools and e learning platforms projects that blend Python, AWS, TensorFlow, NLP and modern web frameworks. I enjoy developing and deploying ML models for computer vision and NLP, designing cloud based dashboards with AWS, Python and Grafana and building web platforms that improve accessibility and collaboration.

Looking for an opportunity to work as a software engineer, data scientist or ML engineer where I can combine reliable code with practical machine learning, help teams ship faster and continue growing through meaningful projects and professional development.

Skills

Certifications

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AWS Certified Solutions Architect - Associate

CISCO Python

IBM Building AI Agents and Agentic Workflows

Languages

Python

C/C++

Java

JavaScript

TypeScript

HTML, CSS

R

C#

Node.js

React.js

Databases

MySQL

MongoDB

NoSQL

PouchDB

CouchDB

Tools & Technologies

NumPy, Pandas, Scikit-Learn, OpenCV

TensorFlow, Keras, Machine Learning, NLP

Matplotlib, Power BI, Tableau

CUDA

Frameworks and Cloud Technologies

Django, Flask, REST API

Amazon Web Services (AWS)

Google Cloud Platform (GCP)

Docker

Kubernetes

Other

Git

Heroku

Streamlit

Jira

CI/CD

Experience

Software Developer @ Chico State Enterprises

Architected a full-stack offline-first platform for field data collection in zero-connectivity environments.

Eliminated data loss for 100+ students in zero-connectivity field environments by architecting a full-stack offline-first web application using React and PouchDB
Reduced data sync errors by 40% by implementing bidirectional CouchDB synchronization that automatically merges updates on reconnection
Accelerated deployment consistency across all environments by containerizing the application using Docker and an NGINX reverse proxy
Increased API reliability and throughput by designing and maintaining RESTful APIs with SQL and NoSQL schemas optimized for high-availability data workflows
Shipped features on a two-week release cycle by leading Agile sprint planning, user stories, and Kanban board execution

Tools: React, PouchDB, CouchDB, Docker, NGINX, REST APIs, SQL, NoSQL, Agile

Dec 2024 - Present | USA

AI Engineer @ NCompas Technology

Designed and deployed ML and deep learning systems integrated into a production SaaS product pipeline.

Served real users in production by designing and deploying ML and deep learning systems integrated directly into a SaaS product pipeline
Improved inference accuracy and reliability across classification, NLP, and image recognition tasks by building and optimizing modular ML models integrated into existing product workflows
Reduced manual SaaS processing overhead by designing and deploying multi-agent LLM systems that automated complex business workflows end-to-end
Increased system uptime and caught regressions early by implementing continuous monitoring, debugging pipelines, and iterative optimization across AI components
Delivered AI features on aggressive timelines by collaborating with product and engineering in an Agile startup environment

Tools: Machine Learning, Deep Learning, NLP, Computer Vision, LLMs, Docker, Agile

June 2025 - Aug 2026 | USA

Software Development Intern @ Christ University

Redesigned and developed a responsive web platform for innovation and startup collaboration.

Boosted platform user engagement by 35% by redesigning and developing a responsive web platform using React and modern front-end technologies
Cut feedback turnaround time by 50% by partnering with the CIC team to implement iterative UX improvements driven by user research and data
Scaled access to innovation resources for 500+ startups, entrepreneurs, and students by building a centralized digital hub for mentorship and collaboration
Delivered the full project lifecycle — from requirements gathering to production deployment — in a structured Agile environment with weekly sprint reviews

Tools: React, JavaScript, HTML, CSS, Agile

May 2023 - May 2024 | India

Machine Learning Intern @ zebo.ai

Built CNN models for dermatology image classification on a production AI platform.

Improved ML training pipeline efficiency by 30% by collecting and preprocessing a dataset of 10,000+ dermatology images for a production AI platform
Achieved 92% validation accuracy on skin condition classification by building a CNN image recognition model using TensorFlow/Keras
Improved model recognition rates 15% above baseline through systematic hyperparameter tuning, regularization strategies, and data augmentation
Delivered a production-ready end-to-end ML pipeline covering data ingestion, preprocessing, training, evaluation, and deployment documentation

Tools: Python, TensorFlow, Keras, CNN, Image Processing

Apr 2022 - May 2023 | Bengaluru, India

Artificial Intelligence Intern @ 1Stop.ai

Developed ML and neural network models for large-scale news article classification.

Reached 94% validation accuracy on a 20,000+ article news classification task by developing ML and neural network models for NLP text classification
Cut model training time by 40% by optimizing data preprocessing pipelines through automated cleaning and feature engineering
Boosted classification accuracy 12% above baseline through systematic model evaluation, hyperparameter tuning, and cross-validation

Tools: Python, Machine Learning, Neural Networks, NLP

Feb 2022 - Apr 2022 | Bengaluru, India

Projects

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Personal Assistant for Windows (AERO - Automated Executed and Response Orchestrated)

A voice-controlled AI assistant with computer vision and object detection capabilities, integrated with AI-powered content generation.

Python AI Computer Vision NLP

Code

1Click - Sycamore and RAG Pipelines

A web-based e-learning platform using Sycamore API and RAG pipelines with modular features including summary extractor, data dashboard, chatbot, and video/quiz integration.

Node.js React AWS Terraform RAG

Code

Education

California State University, Chico

Chico, CA, USA

Master of Science, Data Science and Analytics

2024 - 2026

Relevant Courseworks:

Advance Data Science
Advance Machine Learning
Artificial Intelligence
DevOps Engineering
Data Structures and Algorithms

Christ University

Bangalore, India

Bachelor of Engineering in Computer Science (Artificial Intelligence and Machine Learning)

2020 - 2024

Relevant Courseworks:

Software Engineering
NLP
Design and Analysis of Algorithms
Cloud Computing
Database Management Systems
Operating Systems

Publications

Evaluating Sentiment Classification Models for Bollywood Movies

Springer · May 27, 2025

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The rise in popularity of Bollywood cinema, fueled by streaming services such as Netflix and Amazon Prime, has increased the global availability of Hindi-language films. This study investigates the sentiment analysis of Bollywood movie reviews using a dataset of 1,698 movies released between 2005 and 2017. The research examines three main dimensions: confusion matrix, evaluation metric, and random forest model. The results highlight the model's ability to accurately predict emotions, especially when detecting neutral and positive emotions. Challenges in identifying negative emotions persist.

Enhancing the Recognition of Hand Written Telugu Characters: Natural Language Processing and Machine Learning Approach

IEEE Xplore · Sep 3, 2024

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Handwritten character recognition has wider application in many areas including heritage documents, education, document digitalization, language processing, and assisting the visually handicapped and other related areas. The paper tries to improve the accuracy and efficiency of recognizing handwritten letters of Telugu language scripts, a difficult task for computers. Telugu is most widely spoken language in southern part of India, it has rich cultural heritage. Using the Natural Language Toolkit (NLTK), this study investigates ways to enhance recognition accuracy by analyzing handwritten content and implementing methods such as feature extraction and classification. The purpose is to use NLTK's capabilities to develop handwritten character recognition.

Contact

govardhanreddyb11@gmail.com

(530)-715-9445

github.com/Baddala-Govardhan

linkedin.com/in/baddala-govardhan-reddy-83602a233