Available for new opportunities
Data scientist and software engineer based in New York. I build machine learning systems, extract insight from messy data, and ship full-stack applications that do something real.
What I work with
Machine Learning
Classifiers, clustering pipelines, neural networks, imbalanced-class handling, model evaluation & comparison
Data Engineering
Automated scrapers, scheduled ingestion jobs, data cleaning & normalization, multi-source ETL into PostgreSQL
Full-Stack Web
CRUD apps, REST APIs, relational data models, interactive frontends with state management
Data Analysis & Visualization
Exploratory analysis, statistical summaries, charts & dashboards, written reports for non-technical audiences
Where I've worked
Sept–Dec 2025
Fordham University
Sept 2024–May 2025
Fordham University
Oct 2021–Present
Fordham University
Oct 2019–Present
Self-Employed
Things I've built
A production financial data application that tracks Ginnie Mae mortgage-backed securities (MBS) pools. Raw government data is automatically ingested, processed, and published to a live web interface — updated on a real business-day schedule, multiple times per month.
This is not a demo. The data pipeline runs on a schedule and the application serves real, current financial data to real users.
Neural Networks
Built a fully functional multilayer neural network in plain Python — no ML libraries. Supports configurable layers, neuron counts, and neural suppression.
Classification
Classified books into seven genres using Sklearn. Compared SVM and Naive Bayes classifiers using confusion matrices to evaluate model accuracy.
Data Visualization
Explored biking patterns across New York City using Citi Bike's open data. Pre-processed raw data in Python and built the final visualization in Tableau.
NLP / Clustering
Research project clustering university course descriptions with scikit-learn and comparing automated groupings to existing academic categorizations.
Academic background
M.S. Computer Science
Fordham University — New York, NY
GPA 3.97M.L.S. Library Science
Queens College, CUNY — New York, NY
Certificate in Software Engineering
Fullstack Academy — New York, NY
B.A. Art
Columbia University — New York, NY
Background
My name is Michael, though most people call me Mike. I've been a public librarian for most of my adult life — NYPL, Enoch Pratt, and now Fordham University — which means I spent a long time thinking seriously about how information is organized, found, and made useful to people before I ever wrote a line of Python.
In 2021 I finished a software engineering certificate at Fullstack Academy and discovered I actually loved building things. So I kept going — earning my M.S. in Computer Science from Fordham in 2025 with a 3.97 GPA, while working full time. Along the way I built a live financial data pipeline, taught Database Systems as an adjunct lecturer, and contributed to research on NLP and course descriptions.
The librarian background shapes how I think about data problems — not just how to build the pipeline, but what question is actually worth asking, and how to explain the answer to someone who doesn't want to read a technical report. Twenty-two years of helping people find things will do that.
I studied Art at Columbia, which might seem unrelated but trained me to think carefully about how things look and feel — something that turns out to matter when you're presenting data to people who have to make decisions from it.
I'm currently looking for roles in data science, data engineering, or software engineering. Also happy to talk about freelance work of almost any kind.
Let's work together
I'm actively looking for roles in data science, machine learning, or software engineering. If you have something interesting, I'd love to hear about it.
Send me an email