Sancharika Debnath | Data Scientist

Experience

Graduate Apprentice (CSE)

Air-Gapped LLM Deployment: Deployed LLaMA and Mistral inference stacks across Linux and Windows in a high-security network with no internet access — enabling 12+ researchers to run AI queries locally.
Research Efficiency: Reduced average query turnaround from ~2 hrs to under 35 min by designing a localized model execution pipeline that eliminated all external dependencies.
Mentorship: Guided and mentored a group of 10 interns through their internship project — overseeing technical direction, troubleshooting blockers, and ensuring delivery within the program timeline.
Automated Tender Generation: Built a Tesseract + EasyOCR extraction pipeline feeding a LangChain/Ollama RAG system that outputs structured LaTeX tender documents from noisy scanned PDFs — eliminating manual drafting for 20–30 documents/week.

Product Developer (concurrent freelance)

Voice AI Agent: Built a prompt-orchestrated calling agent using ElevenLabs, enabling 3+ enterprise clients to automate inbound/outbound call flows with configurable tone, persona, and conversation logic.
Cloud Migration: Migrated full production stack from AWS (RDS MySQL + S3) to GCP with under 60 min of downtime — cutting monthly infrastructure costs by 46% ($130 → $70/month).
OCR Automation: Automated business card digitization using Google Vision API, parsing card images into structured vCard and Excel output for a sales team's CRM onboarding process.

AI/ML Engineer

Event AI System: Built a system integrating facial recognition, object detection, and GPS tracking — achieving 87.4% accuracy using OpenCV, YOLO, and DeepFace.
Public API: Delivered an Elasticsearch-powered API covering 1.3M+ Indian education institutes via web scraping and AWS Shell Scripting.
Conversational AI: Built a Dialogflow + GPT chatbot with Firebase integration, growing user engagement from ~800 to ~1,500 sessions/month within the first quarter.

Backend Developer

Django REST API: Built a customized backend for a platform with 50+ advisors, with unit testing, bug fixing, and CI/CD integration. Stack: Python/Django, SQL, AWS, Git, JIRA.

Data Science Intern

Port Clustering: Clustered global port data using Gaussian Mixture Models for cost estimation — stack: PySpark, Databricks, Microsoft Azure.
Logistics Forecasting: Modeled container turn times and forecasted attachment ratios using XGBoost and Regression, contributing to a 15% increase in freight profit.
Customer Segmentation: Applied K-Means clustering to segment large-scale customer datasets, identifying behavioral patterns to optimize digital acquisition strategies.