Data Scientist · Analyst

Nate Wade

Data Scientist  ·  Analyst  ·  Cornell University → Drexel University MSDS

I'm a data scientist and analyst with a passion for building products and finding solutions through data, specifically in healthcare. My journey combines technical growth in AI and ML domains and then applying them where they create a real impact, especially in improving healthcare outcomes and systems. In my free time, you can find me in Philadelphia, PA, where I'm either on the golf course, in the gym, or finding a good bite to eat.

View Projects Resume
~90%
F1 Score,
Healthcare Model
+10%
Call Center
Performance Lift
3+
Deployed
ML Systems
E2E
ETL → Model
→ Deployment

// 01 — Work

Selected Projects

End-to-end ML systems built with real constraints, real data, and measurable outcomes.

Predicted FPS across GPU × Game × Settings
★ Flagship Project

GPU Performance Advisor

Predicts frames-per-second across any GPU, game title, and graphics settings combination — bridging a $400B PC gaming market gap where benchmarks exist for only ~5% of all possible configurations. Features a natural language query interface so non-technical users can ask "Will a 4070 run Cyberpunk at Ultra 1440p above 60fps?" and get an explained answer.

XGBoost Core model
NL Query LLM Interface
Live Streamlit deploy
Python XGBoost NLP / LLM FastAPI Streamlit SQL Fuzzy Matching
Live Demo → GitHub
🏥
Healthcare ML

Hospital Admissions Predictor

The model development work behind my Zinnia Health internship. Applied SMOTE for class imbalance and PCA for dimensionality reduction across patient intake data, then benchmarked Logistic Regression against XGBoost using cross-validation and hyperparameter tuning to select the architecture deployed in production.

PythonSQL Logistic RegressionXGBoost PCASMOTE FastAPIETL Pipelines
100k+
Patient Records
15+
Engineered Features
📈
Research

GPU Performance Prediction Research Report

This study quantifies exactly how GPU specifications influence gaming performance across resolutions and graphical settings. Comparing Linear Regression and XGBoost, then applying SHAP to reveal which hardware features matter most and when. XGBoost explained 70% of FPS variance, outperforming the linear baseline by 16.7%, with pixel rate emerging as the single strongest predictor at +22.5 FPS impact.

PythonXGBoost Linear RegressionSHAP Pandasscikit-learn NumPy
70%
R² — XGBoost
+22.5
FPS — Pixel Rate SHAP
Read Paper →

// 02 — Background

Where I've Built Things

Sept 2026 – Present
Children's Hospital of Philadelphia (CHOP)
Research
AI Research Assistant
Contributing to AI safety research in the mental health department, focusing on clinical guideline compliance and structured conversation design for patient facing systems.
  • Reviewing clinical guidelines and HIPAA compliance frameworks to establish ethics and safety constraints for AI in the mental health department
  • Applying modern AI safety methods including deterministic response sets and structured conversation flows using Azure and Terraform
  • Designing an LLM recommendation system that parses patient survey data to support clinical decisions, defining safety metrics for AI Governance Committee review
Summer 2024
Zinnia Health
Internship
Data Scientist Intern
Built and deployed supervised ML models for medical admissions classification, owning the full pipeline from raw data ingestion through model deployment and KPI reporting.
  • Built and deployed supervised ML models for medical admissions classification using Python, SQL, and FastAPI.
  • Engineered features and performed dimensionality reduction (PCA) for improved model stability across imbalanced clinical data.
  • Trained and evaluated Logistic Regression and XGBoost models using cross-validation and hyperparameter tuning — achieving ~88% accuracy and ~90% F1.
  • Automated ETL pipelines and KPI reporting dashboards using Python and SQL, improving call center performance by 10%.
  • Supported data-driven decision making through model monitoring and performance analysis.
Jun 2026 – Present
White Dog Cafe
Employment
Food Runner
Part time work in Philadelphia while completing my MSDS and continuing the job search.
2022 – 2025
Cornell University
Division I Athletics
Cornell Division I Wrestler
Competed at the Division I level for Cornell's wrestling program while maintaining a full academic load — 20+ hours per week of training, travel, and competition.
  • Committed 20+ hours per week to training, film study, and competition as a full-time student-athlete.
  • Part of EIWA and Ivy League Team Championship winning squads — competing at the highest level of collegiate wrestling.
  • Team 7th, 2nd, and 3rd place NCAA D1 Finishes
  • Developed elite-level discipline, time management, and composure under pressure — skills that directly translate to how I approach complex, deadline-driven technical work.

// 03 — Stack

Tools & Technologies

Modeling
XGBoost scikit-learn Logistic Regression Neural Networks SMOTE PCA SHAP Feature Engineering Hypothesis Testing
NLP / LLM
LLMs PyTorch Text Classification Prompt Engineering NL Query Parsing Fuzzy Matching
Infrastructure
Python SQL PostgreSQL FastAPI Docker AWS Azure Terraform ETL Pipelines Streamlit REST APIs R Java Git Excel

// 04 — Education

Academic Foundation

Incoming · MS
Drexel University
Master of Science
in Data Science
Philadelphia, PA · Expected 2028
Focus Areas
Advanced ML Deep Learning Big Data Systems Applied Statistics
Graduated Dec 2025
Cornell University
Bachelor of Science
Information Science
Ithaca, NY · Data Science Concentration · Dean's List · Major GPA: 3.53
Relevant Coursework
Algorithms Machine Learning NLP Linear Algebra Data Analytics Discrete Structures Python

// 05 — About

The Person
Behind the Models

I'm a data scientist and analyst with a passion for building products and finding solutions through data, specifically in healthcare. My journey combines technical growth in AI and ML domains and then applying them where they create a real impact, especially in improving healthcare outcomes and systems.

I'm pursuing my MS in Data Science at Drexel University and actively looking for data scientist and analyst roles where I can build data-driven solutions, particularly in healthcare, at the intersection of analytical depth and real-world impact.

Beyond the keyboard, I competed as a Division I wrestler at Cornell — 20+ hours a week, EIWA and Ivy League Team Championships. In my free time you'll find me golfing, in the gym, or hunting down a good meal in Philadelphia.

Let's Talk → View Resume
Currently seeking
Data Scientist and Analyst roles in the healthcare sector
Based in
Philadelphia, PA · Open to remote & hybrid
Certifications
AWS Cloud Practitioner · AWS AI Practitioner
Outside of work
D1 Wrestler (Cornell) · Golfer · Gym rat · Food enthusiast

// 06 — Contact

Let's Build
Something Real

Open to full-time data science and analyst positions

Send me an email →
✉
Email
nate.j.wade@gmail.com
→
in
LinkedIn
linkedin.com/in/nate-wade-4221892a1
→
📄
Resume
Download PDF
↓