Currently an Extern @PfizerI'm a data analytics expert leveraging AI to drive insights and efficiency. Let's transform data into action together!
Dive into my portfolio showcasing AI-driven projects, including document intelligence solutions that automate real-world workflows.
.jpg)
Pfizer · ✅ Verified by Extern · ⏱️ In progress
Prototype AI-powered document intelligence with Pfizer—using OCR, LLMs, and RAG to automate real enterprise PDF workflows and build a standout portfolio project.
Developed a predictive modeling pipeline on 50,882 customer records to analyze insurance offer response behavior, performing data cleaning, feature engineering, and multi-model comparison to support customer segmentation. Developed an NLP-based sentiment analysis model to classify customer reviews and identify opinion trends, uncovering key insights such as a strong positive correlation between review length and helpfulness to inform product and service improvement strategies.
I'm a data analytics expert leveraging AI to drive insights and efficiency. Let's transform data into action together!
I am a data analytics professional with a master's degree, reentering the workforce after a career break. My focus is on strengthening my resume and enhancing my competitiveness in the job market.
Externships
Pfizer Advanced AI-Powered Document Intelligence Externship
Pfizer
Experience
SENIOR DATA ANALYST
Star Health and Allied Insurance India · Feb 2020 - Oct 2022
Transportation Data Specialist
Amazon Development Centre India · Aug 2016 - Aug 2017
Education
Springboard Data Science and Machine Learning Bootcamp
Class of 2024
PGP (Data Science and Machine Learning), Great Lakes Institute of Management
Class of 2020
MTech (Biotechnology), Jawaharlal Nehru Technological University
Class of 2019
Skills
✅ Verified by Extern · ⏱️ In progress
Prototype AI-powered document intelligence with Pfizer—using OCR, LLMs, and RAG to automate real enterprise PDF workflows and build a standout portfolio project.
The externship prototyped AI document-intelligence techniques applied to pharmaceutical PDFs. Work explained how tokenization maps to embeddings, how attention scores relationships between tokens, how transformer layers build richer context, and how pretraining plus fine-tuning produce deployed LLMs.
I produced a technical explanation of how LLM architectures convert tokens to embeddings, use attention across tokens, stack transformer layers for context, and rely on pretraining plus fine-tuning to create deployed models.
Faced with the task to explain LLMs, I described how tokens were converted to embeddings, how attention scored relationships across tokens, how transformer layers built richer context, and how pretraining plus fine-tuning produced the final deployed model.
Developed a predictive modeling pipeline on 50,882 customer records to analyze insurance offer response behavior, performing data cleaning, feature engineering, and multi-model comparison to support customer segmentation. Developed an NLP-based sentiment analysis model to classify customer reviews and identify opinion trends, uncovering key insights such as a strong positive correlation between review length and helpfulness to inform product and service improvement strategies.