AJAY
Currently pursuing MS Business Analytics @ WPI
I work at the intersection of business and data, building solutions that turn raw information into clear decisions. With a background in business development and hands-on experience in analytics, I bring both context and execution to the table.
My work spans machine learning pipelines, recommendation systems, and marketing analytics using Python, SQL, and BI tools. I focus on making data not just accurate, but actionable for real-world decisions.
I am also building DataStatz, a no-code automated analysis platform with EDA, AutoML pipelines, and shareable dashboards.
Ajay Ramineni
Data & Business Analyst
0.0 GPA
Academic Excellence
0+
Leads Managed
0K+
Records Analyzed
0+
Projects Built
Selected Work
PROJECTS &
CASE STUDIES
The Compliance Trap
WPI BUS596 capstone. Cross-sectional OLS regression across 11 merged CMS datasets and 2,833 U.S. acute care hospitals. Identified three systemic failure modes in federal penalty programs: infection metric blind spots, readmission displacement, and multi-program convergence. Includes an interactive hospital explorer.
// Key Highlights
- ▸Merged 11 CMS public-use files across 2,833 U.S. acute care hospitals
- ▸Ran 9 OLS models with HC3 robust SE — all key findings at p < 0.001
- ▸Built interactive research site with live hospital explorer
DataStatz
No-code automated data analysis platform. Upload a CSV or Excel file — get instant EDA, cleaning diagnostics, ML feasibility scoring, and structured insights without writing a single line of code. 6-service FastAPI backend with an AutoML pipeline running 5 simultaneous models.
// Key Highlights
- ▸6-service FastAPI backend: Parser, Cleaning, EDA, Scope, Insight, AutoML
- ▸AutoML pipeline running 5 simultaneous models with ranked comparison and confidence scoring
- ▸Supabase Postgres for persistent report sharing and stateless OTP auth
Azure ML Income Prediction
End-to-end ML pipeline for income prediction using boosted decision trees on Azure ML Studio. Includes data preprocessing, feature engineering, hyperparameter tuning, and model deployment. Achieved strong AUC-ROC on the Adult UCI dataset.
// Key Highlights
- ▸Built full pipeline from raw data to deployed model on Azure ML Studio
- ▸Applied feature engineering, cross-validation & hyperparameter tuning
- ▸Achieved optimised AUC-ROC using ensemble boosted decision trees
EchoForge AI
Self-hosted voice synthesis backend using Coqui XTTS v2. Clones and consistently reproduces an assistant-style voice from a single short WAV reference clip — no training required. Production-ready REST API with automatic GPU/CPU detection and cached conditioning latents for low-latency inference.
// Key Highlights
- ▸Clones a consistent voice identity from a single WAV clip — no training needed
- ▸Sentence-aware synthesis with cached GPU/CPU latents for low-latency responses
- ▸Production-ready FastAPI backend with /speak, /health, and /info endpoints
Tech Stack
SKILLS & TOOLS
Frontend
Backend & APIs
Machine Learning
Data & Databases
Business Intelligence
Tools & Platforms
Writing
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OPEN TO DATA, ML & BI ROLES
Actively looking for opportunities in data analytics, business intelligence, and machine learning.