Data Scientist transforming raw data into actionable insights at the intersection of analytics, machine learning, and business intelligence.
I’m a Data Scientist passionate about turning complex data into intelligent, real-world solutions. I focus on machine learning, analytics, and scalable AI systems. From data discovery and feature engineering to model selection, deployment, and monitoring, I love transforming ambiguity into measurable impact and creating solutions that truly make a difference.
Python
Scikit-learn
XGBoost
Keras
Hugging Face
spaCy
LlamaIndex
LLM APIs (OpenAI, Anthropic)
Ollama
Pydantic
RAGAS
Streamlit
DVC
Docker
Linux
At Octazen, customer churn was climbing and no one really knew why. I wasn’t handed a clean dataset or a clear plan, just a problem that needed fixing. I started with a quick logistic regression baseline, moved through Random Forest and XGBoost, and ended up with a model that held its ground 88% accuracy and 0.91 AUC. But models in notebooks don’t change outcomes, so I built a FastAPI microservice with versioned /score and /health endpoints, turning it into something teams could actually use in production.
Meanwhile, I teamed up with data engineering to clean up the backend mess smarter indexes, tighter joins, fewer round trips which cut query latency by 35%. Suddenly, dashboards started updating faster and churn risk wasn’t a mystery anymore. My biggest takeaway? Great models matter, but impact comes from shipping systems that work every single day.
Turns your Notion workspace into a conversational, context-aware assistant with semantic search, Retrieval-Augmented Generation, and real-time answers.
Interactive Tableau dashboard that surfaces early-tenure and travel-related attrition drivers; current overall attrition 16.1%.
End-to-end ETL from API / web scraping / CSV → pandas transforms → SQLite → a sleek Streamlit app. Containerized with Docker and schedulable with Airflow.
Turns LinkedIn job posts into live, voice-driven mock interviews with instant AI scoring and natural TTS - right on the job page.
University of North Texas, USA GPA: 4.0/4.0
Focused on Machine Learning, Analytics, and Artificial Intelligence with hands-on projects in
data modeling, visualization, and deployment.
Malla Reddy College of Engineering, India GPA: 3.56/4.0
Gained strong foundations in Algorithms, Data Structures, and Databases with experience in full-stack and software engineering projects.
Amazon Web Services
Validates expertise in building, training, tuning, and deploying machine learning models on AWS. Demonstrates proficiency in MLOps, SageMaker, model optimization, and production ML systems.
MongoDB University
Demonstrates proficiency in using Python with MongoDB, including PyMongo driver, CRUD operations, aggregation pipelines, indexing, and building Python applications with MongoDB.
Open to roles and collaborations in data science, ML, and analytics. Prefer email first.