About Me
I am an experienced production and technical manager turned data and AI engineer with a demonstrated history of success in manufacturing.

My demeanor is often described by others as high-energy, detail oriented, self-motivated, and forward thinking.

I’ve managed up to 300 employees at a time with successful leadership assignments across multiple manufacturing facilities (production supervisor, quality superintendent, production superintendent x4, and plant manager).

I’ve formally studied mathematics, computer science, business, statistics, and data science across 5 universities over the last 20 years.

I’m fluent in SQL, Python, and Docker with an expertise in pipeline orchestration (from scratch), time series data analytics/ML, and data science applications in manufacturing.

I can leverage:

• SQL, Pickle, Parquet, CSV
• Docker (CLI and desktop)
• APIs, webhooks (Flask, FastAPI)
• Git CLI
• Azure DevOps (Container Apps, Azure Functions, ADLS)
• Juypter Notebooks, Google Colab, Visual Studio, MS SSMS
• Pandas, Numpy, Matlab, Seaborn, Plotly, Statsmodel, Sckitlearn, PyTorch, and many others
• Exploratory Data Analysis (data cleaning and visualizations)
• Statistical Testing (sampling, hypothesis testing)
• Baysian Statistics (all flavors, Naive, Markov Chains, decision science applications)
• Dimensionality Reduction (PCA, FPCA, etc.)
• Clustering Analysis (KNN, DBScan, etc.)
• Regressions (OLS, Lasso, Ridge)
• Decision Trees (XGBoost, Random Forest)
• Neural Networks (deep, CNN, RNN)
• Structured and unstructured data scraping
• Natural Language Processing (vector and RAG from scratch, prompt engineering, API orchestration, etc.)
• Stochastic Optimization (Model Predictive Control, etc.)
• Reinforced Learning (Q-Learning, MDP, etc.)
• Allen-Bradley PLC (PLC5, Studio 5000 - tag browsing, handshakes, for data movement)
• Power BI (dashboarding, pipelines)
• Streamlit (advanced level)
• Flask + Jinja w/ HTML/CSS/JS

My career vision is to bridge the gap between management, production, IT, and accounting by solving big problems with automation and data science and then teaching the methodology to everyone who will listen.