E-mail: zachapell220@gmail.com
Phone: (856) 298-0064
Linkedin
Resume
As a strategic problem solver, I develop data products using the Data Science lifecycle, machine learning, and statistical analysis. At the core of my success is my ability to effectively collaborate with business partners, data engineers, and machine learning engineers to create reliable production solutions with domain specific expertise. By clearly translating business challenges from technical analysis to digestible insights, I provide instant value to cross-functional teams. Swiftly learning new technologies and languages within the scope of a full machine learning problem energizes me.
In the fall of 2024 I will be pursuing a masters in biostatistics.
I have been a Data Scientist on the Machine Learning Banking team at Rocket Mortgage. Our team's goal was to optimize the lead allocation and mortgage banking space. This includes lead prioritization and how bankers interact with loans as they move through their pipeline. There are some more details here
At Freedom Mortgage, I was a Data Engineer and Data Scientist on the Data Science team at Freedom Mortgage. By moving through different teams such as ETL, Data Governance, Marketing Analytics, and the Intelligent Automative Initiative, I have developed an enterprise wide understanding of the data flow which has been an immense help in my ability to get the right data for modelling. Additionally, I have met with business leaders to understand problem areas and work with them to develop ways that our team can provide business value.
I graduated in May 2020 from the University of Michigan studying Data Science. I have taken courses in Computer Vision, Machine Learning, Data Mining, Applied Regression Analysis, Bayesian Data Analysis, Web Systems, Database Management Systems, and Honors Multivariable Calculus. I have experience coding in a variety of languages but in my daily work I primarily use Python and SQL.
In summer 2019, I interned for Wacker Chemical Corporation, an international chemical company for highly developed specialty chemicals. I was a part of the new Data Science team where I led the sales forecasting project for a business team. Using models like SARIMA, LSTM, and SVR I was able to develop a model that improved the current forecasting tool by 10% less deviation in 2019. Another fun project I worked on was Natural Language Processing of Twitter data. I scraped tweets containing specific keywords and hashtags pertaining to our customers and their market segments. We did some sentiment analysis of these tweets and presented our findings to the marketing managers.
Outside of work, I am a huge sports junkie, specifically Philadelphia and Michigan sports. I am also passionate about music and in the future I plan on applying data science to music with the hope of creating algorithmic based music. If you have any ideas or interest in this, I would love to talk about it!
Below are some of the school and personal projects that I have had the pleasure to work on. There is a wide variety of applications demonstrated in my catalog and I very much enjoy trying and learning from new challenges.
This course is an introduction to 2D and 3D computer vision. Topics include: cameras models, the geometry of multiple views; shape reconstruction methods from visual cues: stereo, shading, shadow, contours; low-level image processing methodologies such as edge detection, feature detection; mid-level vision techniques (segmentation and clustering); Basic high-level vision problems: face detection, object and scene recognition, object categorization, and human tracking
Project 1: Image Alignment
The class focused on the theory and implementation of state of the art machine learning algorithms for large-scale real-world problems.
Project: Landmark Classification