Pfizer Externship via Extern
Pfizer Externship via ExternI use AI, data science, Python, R, SQL, and analytics to transform complex business data into actionable insights that enhance marketing, product performance, and revenue growth.
My portfolio showcases projects from my externship at HP Tech Ventures, where I analyze and evaluate emerging tech startups.
Breaking Games · Aug 2026 · ✅ Verified by Extern
Turn six messy CSVs into an analytics database and a Q4 dashboard that drives real business decisions. SQL + Claude.
Wayfair · Oct 2026 · ✅ Verified by Extern
Learn one of the most in-demand skills shaping the future of work: building AI agents. In this externship, you’ll step into the role of an AI Agent Developer and use n8n to design agents that think, plan, and act on real data — automating insights, decisions, and workflows. No coding experience required. By the end, you’ll create a live dashboard powered by your own AI agents, gaining hands-on experience that applies to any role or industry — from marketing and consulting to product management and data science.
Cleaned and analyzed retail data using Python to evaluate customer behavior, promotions, sales, and store performance. Applied feature engineering and evaluated Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting models using classification metrics. Built a Power BI semantic model with fact and dimension tables and developed DAX measures for revenue, customers, orders, and elasticity-related KPIs.
Analyzed 50,000+ flight records to identify cancellation, delay, and on-time performance trends. Built a star-schema data model and interactive Power BI dashboard using DAX measures and operational KPIs.
Analyzed customer purchasing behavior using R, tidyverse, and statistical methods to identify customer and product trends. Compared trial and control stores using correlation, magnitude-distance measures, and uplift analysis to evaluate trial performance.
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.
I use AI, data science, Python, R, SQL, and analytics to transform complex business data into actionable insights that enhance marketing, product performance, and revenue growth.
Business analytics professional using AI, data science, Python, R, SQL, Power BI, and Databricks to transform complex data into actionable insights. Through my Breaking Games externship, I analyzed e-commerce, marketing, and product performance to support smarter decisions and business growth.
Externships
Pfizer Advanced: AI-Powered Document Insights & Data Extraction Externship
In progress
Experience
Pricebook Associate
7-Eleven · Dec 2023 - May 2026
Education
East Texas A&M University
M.S in Business Analytics · Class of 2025
Skills
✅ Verified by Extern
Turn six messy CSVs into an analytics database and a Q4 dashboard that drives real business decisions. SQL + Claude.
The work converted six raw CSVs into a populated SQLite analytics database, cataloging sources and capturing five immediate answers in a Holiday Game Plan. The contributor reconciled four disjoint tables into a dim_product table with stable IDs, joined sessions, carts, and sales, and attributed revenue to Meta campaigns. The output included a Q4 ad allocation plan and a clean_checkouts view that
I built a populated SQLite database from six CSV sources, authored SQL that returned five direct answers (top products by units and revenue, catalog size, ad spend, checkout sessions), created a dim_product table with stable IDs and join logic, delivered a campaign-level Q4 ad allocation plan, and produced a clean_checkouts view that excluded bot traffic.
✅ Verified by Extern
Learn one of the most in-demand skills shaping the future of work: building AI agents. In this externship, you’ll step into the role of an AI Agent Developer and use n8n to design agents that think, plan, and act on real data — automating insights, decisions, and workflows. No coding experience required. By the end, you’ll create a live dashboard powered by your own AI agents, gaining hands-on experience that applies to any role or industry — from marketing and consulting to product management and data science.
The project implemented multiple n8n AI agents that converted style briefs into image prompts, fetched and processed e-commerce and social data, merged competitor product datasets, and compiled PDF reports and a linked dashboard. The work was awarded Top Performer by Wayfair.
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I built a Moodboard Generator and three additional n8n AI agents that produced image prompts, an Area Rug Trend Report (PDF) with demo video, a competitor analysis PDF, a content strategy PDF with walkthrough, and a combined dashboard and presentation.
Cleaned and analyzed retail data using Python to evaluate customer behavior, promotions, sales, and store performance. Applied feature engineering and evaluated Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting models using classification metrics. Built a Power BI semantic model with fact and dimension tables and developed DAX measures for revenue, customers, orders, and elasticity-related KPIs.
Analyzed 50,000+ flight records to identify cancellation, delay, and on-time performance trends. Built a star-schema data model and interactive Power BI dashboard using DAX measures and operational KPIs.
Analyzed customer purchasing behavior using R, tidyverse, and statistical methods to identify customer and product trends. Compared trial and control stores using correlation, magnitude-distance measures, and uplift analysis to evaluate trial performance.
✅ 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 project prototyped AI-powered document intelligence for enterprise PDFs, combining OCR, large language models, and retrieval-augmented generation. The work reviewed model capabilities and integration points, and produced explanatory material on LLM mechanics and their role in document extraction workflows.
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I summarized how LLMs learn language patterns, tokenize input, use Transformer attention to model context, generate token predictions, and undergo fine-tuning with examples and feedback to improve accuracy.
I summarized how LLMs trained on large text corpora learn language patterns, tokenize input, use Transformer attention to model context, predict tokens to generate responses, and undergo fine-tuning with examples and feedback to improve accuracy.
