Currently an Extern @Pfizer
Data Analyst | Python, SQL & Power BI
My portfolio includes externship and university projects: AI document extraction with Pfizer, sponsorship ROI pipelines for NASCAR NY Racing, consumer insights for Beats by Dre, operational people
Beats By Dre · Dec 2025 · ✅ Verified by Extern
A data analytics externship with Beats by Dre focused on extracting qualitative and quantitative consumer insights from surveys and reviews.
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Amazon · Mar 2026 · ✅ Verified by Extern
Imagine shaping how AMAZON — yes, THE Amazon — welcomes and keeps its workforce. Every year, Amazon loses billions because new hires leave before Day 90.You’re going to fix that. In this externship, you’ll web scrape real-world employee reviews from across the web, extract actionable insights, and design persona-driven strategies to help Learning Ambassadors connect smarter, faster, and more humanely.It’s part research, part strategy—and all about learning how people data drives big decisions.
Data Analyst | Python, SQL & Power BI
Data Analyst skilled in SQL, Python, Excel, Tableau, Power BI, data quality, visualization, and business reporting. Experienced transforming complex datasets into accurate insights, performance metrics, dashboards, and actionable stakeholder recommendations.
Externships
Pfizer Advanced: AI-Powered Document Insights & Data Extraction Externship
Pfizer
Experience
Data Analyst
FL Tech Solutions LLC · Jul 2026 - Present
AI-Powered Document Analytics Extern
Pfizer Advanced · Jun 2026 - Present
Data Analytics Extern
NASCAR NY Racing · Apr 2026 - Jun 2026
Consumer Insights Data Analyst Extern
Beats by Dre · Sep 2025 - Dec 2025
Production & Data Analytics Intern
Siemens Industry Incorporated · Jan 2022 - May 2022
Education
University of New Haven, New Haven, CT
Master of Science in Information Science · Class of 2025
National Institute of Technology, Kurukshetra, India
Bachelor of Technology in Production & Industrial Engineering · Class of 2023
Skills
✅ Verified by Extern
A data analytics externship with Beats by Dre focused on extracting qualitative and quantitative consumer insights from surveys and reviews.
The Beats by Dre externship combined survey analysis, text analytics, visualization, and slide-driven synthesis. The work defined target segments, analyzed a 4,000+ respondent wireless-speaker survey, performed sentiment analysis on customer reviews, and produced a five-slide executive plan summarizing market findings.
I produced an Ideal Consumer List, cleaned and analyzed a 4,000+ respondent survey, ran TextBlob sentiment scoring on customer reviews with comparative visualizations, and drafted a five-slide executive plan answering a scoped product question.
✅ Verified by Extern
Imagine shaping how AMAZON — yes, THE Amazon — welcomes and keeps its workforce. Every year, Amazon loses billions because new hires leave before Day 90.You’re going to fix that. In this externship, you’ll web scrape real-world employee reviews from across the web, extract actionable insights, and design persona-driven strategies to help Learning Ambassadors connect smarter, faster, and more humanely.It’s part research, part strategy—and all about learning how people data drives big decisions.
The work analyzed employee voice from fulfillment centers and built operational artifacts used across the externship. They mapped roles and responsibilities into a structured workforce spreadsheet, converted multimedia employee comments into an annotated dataset, and compared sentiment and keywords across platforms. The project also identified burnout during peak season as a priority theme and
I delivered a portfolio of artifacts: a shareable workforce map, a 10-video annotated transcript dataset, cross-platform sentiment and keyword charts, a themed and root-cause analysis identifying peak-season burnout, segmented friction maps, and a pilot plan with checklist, buddy program, ownership, scope, and metrics.
✅ Verified by Extern
A NASCAR NY Racing externship focused on measuring sponsor visibility and building a data pipeline to score sponsorship ROI across a full Cup Series season.
The work built a season-level data pipeline and analytics workflow to measure sponsor visibility for NY Racing. The project set up API access and validated public race data, cleaned and merged 36 race files with social and news sources, and produced normalized visibility scores and season sponsor rankings. The deliverables included cleaned CSVs, a Python scoring implementation, and an executive
I produced a validated, analysis-ready dataset for all 36 Cup Series races, implemented a Python-based visibility scoring model with normalization rules, and compiled an executive report and presentation containing scenario-driven sponsor rankings.
✅ 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 an AI-powered document intelligence pipeline for enterprise PDF workflows, combining OCR, LLMs, and retrieval-augmented generation. Work included building model and NLP foundations and integrating those components into document analysis and data-extraction prototypes. Deliverables documented model choices, processing steps, and prototype outputs.
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I produced a written summary explaining LLM tokenization, the Transformer attention mechanism, and how fine-tuning plus human feedback affect model usefulness and safety.
I explained how large language models tokenized text, used the Transformer architecture and attention to model context, and were trained on large corpora then fine-tuned with additional training and human feedback to improve accuracy and safety.
Designed three Tableau dashboards tracking sales KPIs, trends, forecasts, and monthly performance variances. Built a Python pipeline processing 10,000+ records for cleaning, transformation, classification, and evaluation. Developed SQL queries and relational models supporting repeatable reporting, validation, and business analysis.
