Hi, I'm Nadine Shill. I'm a data-driven researcher and AI engineer specializing in applied mathematics and machine learning.

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Education & Experience

Johns Hopkins University
Master of Science in Applied & Computational Mathematics (Nov. 2022 – Current)University of Colorado Denver
Bachelor of Arts in Psychology (Aug. 2015 – Dec. 2021)Solari
AI/ML Engineering Intern (Feb. 2025 – Current)Johns Hopkins University
Statistics & Probability Learning Assistant (Jan. 2025 – Current)Scale AI
Mathematics Senior Reviewer/Queue Manager (Jan. 2023 – Feb. 2025)University of Minnesota
Quantum Computing Researcher (Jan. 2024 – Mar. 2024)

Projects

Anomaly Detection Fraud on XGBoost: Model trained and deployed to detect credit card fraud.
Unsupervised Learning Clustering using Kmeans: Clustering model trained on housing data.
LSTM Project (RNN): Predicting text based on prior sequences.
Best Route for Wildfire Navigation Model: Calculated shortest routes using public APIs.

Certifications

Machine Learning/AI Engineer Path (Codecademy)
Deep Learning Specialization (deeplearning.ai)
Machine Learning Specialization (deeplearning.ai)

Skills

Python (Django, FastAPI), SQL (MySQL, PostgreSQL), NoSQL (MongoDB)
ML architectures (MLFlow, TensorFlow, PyTorch, LLMs, Deep Neural Networks)
Data Analysis, Data Visualization (Tableau, Pandas, Statistical Testing)
Application Deployment on Streamlit, AWS, Azure; Git, Confluence

Get In Touch

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