🟢 Open to work
As an M.S. Data Analytics Engineering student, I specialize in statistical analysis and applied machine learning, with hands-on projects including Markov models for baseball and RAG pipelines in
This portfolio showcases my projects, including a model analyzing 1.27 million MLB plays and a March Madness bracket predictor with 94% accuracy, each addressing specific challenges.
Self · Jul 2026
I researched pitcher quality in relation to situational leverage. I reformulated the RE24 run-expectancy matrix with Markov processes, analyzing 1.27 million Statcast plate appearances. I created a framework to differentiate game leverage from pitcher quality, revealing a significant impact of
Apr 2025
Developed an ordinal logistic regression model to classify NCAA tournament seedings from team-level and contextual features, achieving 94% classification accuracy (within ±1 seed) on held-out data. Delivered an analytical report and presentation translating model outputs into actionable insights for
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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.
As an M.S. Data Analytics Engineering student, I specialize in statistical analysis and applied machine learning, with hands-on projects including Markov models for baseball and RAG pipelines in
I am Kaleb Jordan, a Northeastern University graduate student pursuing a Masters in Data Science. I have some practical experience and I am completing the Pfizer Advanced: AI-Powered Document Insights & Data Extraction externship, where I worked on AI methods for extracting insights from documents.
Externships
Pfizer Advanced: AI-Powered Document Insights & Data Extraction Externship
Pfizer
Experience
Data Analytics Extern
Pfizer (via Extern) · Aug 2026 – Oct 2026
Machine Learning & AI Intern
Applied Technologies, University of Illinois · Aug 2024 – Dec 2024
Business Process Improvement Intern
Applied Technologies, University of Illinois · May 2024 – Aug 2024
Education
Northeastern University
M.S., Data Analytics Engineering
University of Illinois Urbana-Champaign
B.S., Statistics; Data Science DISCOVERY Certificate · Class of 2025
Skills
Self · Jul 2026
I researched pitcher quality in relation to situational leverage. I reformulated the RE24 run-expectancy matrix with Markov processes, analyzing 1.27 million Statcast plate appearances. I created a framework to differentiate game leverage from pitcher quality, revealing a significant impact of
I researched pitcher quality in relation to situational leverage. I reformulated the RE24 run-expectancy matrix with Markov processes, analyzing 1.27 million Statcast plate appearances. I created a framework to differentiate game leverage from pitcher quality, revealing a significant impact of
Apr 2025
Developed an ordinal logistic regression model to classify NCAA tournament seedings from team-level and contextual features, achieving 94% classification accuracy (within ±1 seed) on held-out data. Delivered an analytical report and presentation translating model outputs into actionable insights for
Developed an ordinal logistic regression model to classify NCAA tournament seedings from team-level and contextual features, achieving 94% classification accuracy (within ±1 seed) on held-out data. Delivered an analytical report and presentation translating model outputs into actionable insights for
✅ 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.
This externship prototyped an AI document-intelligence pipeline combining OCR, large language models, and retrieval-augmented generation to process enterprise PDFs. The work reviewed LLM fundamentals and their role inside a document-extraction pipeline, and produced technical artifacts used across the prototype.
I worked through core technical foundations and built supporting deliverables for a prototype pipeline that combined OCR, LLMs, and RAG for enterprise PDFs.