I’m a data engineer and scientist with experience in managing projects, building machine learning models, and designing real-time dashboards. I enjoy analyzing data and creating systems that improve business performance.
I’ve applied my skills in various arenas, from manufacturing to machine learning competitions. Whether it’s blending predictions using my custom hill-climbing algorithm or streamlining data pipelines, I’m always excited to find the story hidden in the data.
Let’s connect and explore the ways I can contribute to your team!
My work has included large-scale project management, Python module development for machine learning/data analysis, and high-level Kaggle competitions. I bring both technical skills and a problem-solving mindset to every project I work on.
I led the iDM 4.0 project (iNOEX Data Management), building machine learning models, real-time dashboards, and automated reporting for global manufacturers, while ensuring seamless data integration and client support.
At Acero Precision, I sharpened my problem-solving skills by programming CNC machines and optimizing tool paths for surgical medical parts, building the technical precision I now bring to data engineering and machine learning.
As an experienced participant on Kaggle, Google’s data science platform, I’ve uploaded custom datasets, shared code showcasing my data analysis, and developed machine learning models to compete in various challenges.
Active Impulse, an e-commerce platform for Polaris vehicle parts, was my first entrepreneurial challenge. I wore many hats as the business grew: database design, front-end development, and optimizing data handling and inventory.
In my quest for efficiency, I’ve developed tools and solutions for data analysis and machine learning. I've had a great deal of fun and gained valuable knowledge doing these. This collection of projects demonstrates my dedication to continuously improving my skills.
I built fasteda, a Python module for rapid exploratory data analysis of DataFrames. It offers key statistics, correlation plots, and visualizations like histograms and pairplots, with enhanced support for binary classification datasets.
hillclimbers is a Python module I developed that blends machine learning model predictions using hill climbing to optimize performance. It selects diverse models and adjusts weights to improve the desired evaluation metric, and helped me achieve two 4th place finishes in Kaggle competitions.
Geoguessr is a game where you are dropped into google street view in a random part of the world and your objective is to guess exactly where you are. Check out my YouTube video where I demonstrate how I used the Geoguessr API to build this dataset, perform analysis, and create visualizations.
I’ve achieved top rankings in various machine learning competitions, including high placements in regression, classification, and feature imputation tasks. These contests have covered a wide range of topics, such as predicting region flooding, wild blueberry yield, academic risk of students, the probability of an instance being a highly magnetized rotating neutron star, feature imputation in a heat flux dataset, and employee attrition.
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These are the skills I have developed the most expertise in for working with data in a diverse set of contexts.
I’m actively seeking opportunities in data analysis, data engineering, or machine learning. If you're looking for someone who can bring strong analytical skills and technical expertise to your team, feel free to reach out!