Fandom | Full Stack Software Engineer (Team Lead)
July 2016 - Present
- Lead projects for front-end apps and back-end microservices at scale to serve our 200MM uniques
- Data driven management
- Actively seek and hire globally-distributed talent
- Technical lead for Fandom’s next-generation contribution platform
- Mentor engineers from The Last Mile Program
- Work closely with product to align goals
- Lead technical code sharing groups for new technology and programming patterns
- Rolled out WordPress as a Service (Laravel+Webpack+WP) for a fast-to-market editorial solution
- Work closely with international teams
- Data driven development executing A/B tests to validate ideas
Fireman Creative | Full Stack Software Engineer
July 2015 - July 2016
- Independently led several web projects end-to-end
- Collaborated with clients and graphic designers to craft a consistent look and feel of web applications
- Generated monthly SEO and analytical progress reports to track progress
- Created a new container-based deployment process that greatly sped up development flows
- Implemented automated alerts and scripts to restart crashed applications and servers
Pittsburgh Quantum Repository Group | Full Stack Software Engineer
January 2015 – January 2016
- Built python-powered front-end application to display the largest molecule database
- Utilized WebGL technology to create 3D visualizations of molecules
- Built and maintained a MongoDB server
Pittsburgh Genetics | Full Stack Software Engineer
October 2014 – January 2016
- Built a genetic population simulator web application
- Utilized multi-threading web workers to speed up simultaneous simulations
Independent Consultant | Full Stack Software Engineer
January 2012 – Present
- Worked with professional baseball teams, writers, students, and professors to build web applications
- React, redux, wepback, styled components
- Java (gradle, guice, jackson, jersey, jooq, liquibase), Python, PHP (Laravel, Composer, WP), Node.js
- MySQL, MongoDB
- Kubernetes, VCL, AWS
- Data Engineering
- Sklearn, tensorflow, pandas, AWs Glue
MLB Stats Project
Baseball has been one of the strongest influences on my life. I grew up watching it nearly every night with my Dad and playing all year long (in some form) through high school. I love the all of the nuances in the sport. As a baseball evangelical, I spend every free moment taking in baseball. From the front office economics to the 9th inning ejection, I simply can’t get enough. This derangement (I wanted to say passion but my girlfriend disagreed), has led me down the analytics rabbit hole that each team’s front office is competing against one another to uncover some novel stat ignored by moneyball.
pitchFX Data Analysis
Major league baseball has been releasing data of every single pitch thrown for the last 5+ years. This dataset doesn't just contain the velocity and angle but includes things like launch angle, vertical break, horizontal break, release rotation, height the pitch was released at, exit velocity and much more.
I've been looking for a project to utilized machine learning techniques and this dataset was begging to be analyzed.
I had used some basic machine learning tools in the past such as linear support vector machines, and k-nearest neighbor's. But I really want to try to our neural networks. My initial findings indicated that LSVM outperformed neural networks in speed and accuracy. I'm skeptical of these results and wonder if I have a flaw in the setup of my neural network
I started building a UI that allows the user to position a pitch in a virtual strike zone, give it velocity, breaking, and angle variables and the app will determine the likelihood of a hit.
Political Article Analyzer
I find myself more and more frustrated in my search for fair news. The internet lends itself to creating "click-bait" like article titles which isn't great in the search for truth. I started creating a tool that can detect the political leaning of individual articles through natural language processing techniques. Currently, I am attempting to first analyze text and determine the publisher by computing term frequency–inverse document frequency among other things. Next, I would like to investigate sentiment analysis of images. I believe each news outlet choose less flattering images of their "opponent" and I'm curious if the data bears that out.
I also discovered there is an API that attempts to do this: AllSides. I find their ratings quite fair and will use that as a sample set to compare my results.
Some of the code lives in this repo
There is a small front end app I'm using to collect data: Left-Right-Center Data Collection. If you find an article very biased you can copy/paste the link and rate it.