Résumé

Languages & Tools

Python, SQL, Git, Jira, Azure DevOps, PyCharm, CLI

Skills

Natural Language Processing (NLP), Network Analysis/Graph Theory, Machine Learning, Statistics, API (Develop & Consume), Classification, Regression, Data Visualization, Unsupervised Learning, Web Scraping/Crawling, Code Review, Object Oriented Programming, Automation, Unit Testing

Third-Party Packages I Commonly Use (Clickable)

pandas, scikit-learn, networkx, pydantic, numpy, scrapy, matplotlib, seaborn, pre-commit, black, isort, flake8, mypy, requests, spacy, typer, pytest, beautifulsoup4, streamlit, tqdm, fastapi

Career

Senior Data Scientist @ H&R Block

04/2022—present

• Developed a proof of concept to convert tax instructions into a directed acyclic graph using named entity recognition and a key file.
• Using topological sorting within a directed acyclic graph, predicted hundreds of tax lines in parallel within the same generation.
• Through the use of graph algorithms, developed methods to quickly identify which lines in a tax form impacted other lines. This was used to determine where to focus attention when reviewing tax returns for issues that could have a large impact on individual clients.
• Published the subpackage, “ReturnAssistant,” with the ability to process 60,000 prior year returns in ~200ms.
• Crawled the IRS website, extracted instructions for all forms and schedules, and preprocessed into training data.
• Developed and trained a relation extraction pipeline to automate the construction of a directed acyclic graph using a custom tokenizer, a fine-tuned named entity recognition component, and a custom trained relation extraction component.
• Hand-picked for the generative artificial intelligence research and development team that was tasked with researching how emerging technology in the natural language processing space could be leveraged by the company.
• Developed a Python package to help navigate and validate the company tax engine using graph algorithms.

Data Scientist Principal @ The University of Kansas Health System

10/2021—04/2022

• Automated PRM ticket creation saving 100 minutes per month.
• Introduced Gaussian Processes (Kriging) as a data generation technique to estimate values where observations were not recorded in a time series.
• Created and maintained a private Python package to streamline my team’s work.

Business Intelligence Statistician II @ The University of Kansas Health System

06/2019—10/2021

• Deployed a classification model via API to label cost accounting codes with 95% accuracy, shortening manual labeling time from 3 hours to seconds widening the user-base.
• Automated weekly JIRA tickets, time checks, and comment reminders using Python and the JIRA API saving 100 minutes per week.
• Using a topic model, trained a named entity recognition pipeline on charge code descriptions to identify profitable subsets.
• Cut average costs per primary complete mastectomy from $9,000 to $5,500 using evidence provided via bootstrapping and difference testing.
• Deconstructed and rebuilt the COVID-19 CHIME model using internal data, providing an auto-updating dashboard to senior leadership to help with capacity and supply planning.

Statistician @ Cerner Corporation

05/2017—05/2019

• Designed and automated standard analyses, updating PowerPoint presentations directly.
• Simulated and optimized complex rule-based algorithms for patient-provider matching.
• Lead quarterly Python classes to teach and train associates.
• Implemented quality-improvement benchmarks for health systems using a combination of the Achievable Benchmarks of Care and mathematically derived metrics.
• Developed a Python module to generate and execute SQL, allowing the validation of data sets and simulation of changes.

Education

B.S. Mathematics—Kansas State University

08/2013—08/2015

World Affairs—University of Oxford

06/2013—07/2013

Mathematics—Saint Charles Community College

08/2011—06/2013

Achievements

• Mapping Tax Structures Via Natural Language Processing Generated Directed Acyclic Graphs (Patent Application #18/414,771)—HRB Innovations
• Bronze Pandas Tag Badge—Stack Overflow
• Bronze Python Tag Badge—Stack Overflow
• 66 Days of Data Shoutout—66 Days of Data
• A Generalization of the Goresky-Klapper Conjecture, Part I—Society for Industrial and Applied Mathematics (SIAM)
• Consulting Project Eagle Award—Cerner Corporation
• DataCon 2017 Data Competition Winner—Cerner Corporation

Open Source Contributions

• [BUG] – Arguments enable and disable not working as expected in spacy.load—spaCy
• [ENH] – Option to exclude model_extra from repr—Pydantic
• BUG – Requiring name argument in StackAPI makes “/users/{id}/network-activity” endpoint inaccessible—StackAPI
• Update index handling in PandasAdapter—Scikit-learn
• Most recent scikit-learn results in several failed unit tests—mlxtend
• Integrate scikit-learn’s set_output method into TransactionEncoder—mlxtend
• ENH – Replaced for-loops in :function:rescale_layout with numpy vectorized methods.—Networkx
• Typo fix in Language.replace_listeners docs—spaCy
• DOC Update MLPRegressor docs—Scikit-learn
• add feature_names_in_ attribute to FeatureUnion—Scikit-learn
• synset pos parameter—spacy-wordnet

Presentations

• Detecting Kidney Stone CPT Communities using the Louvain Method—Health Analytics Summit @ Health Catalyst (09/2021)
• Exploring Null Space—DataCon @ Cerner Corporation (09/2018)
• Automating Excel Report Generation with Python—DataCon @ Cerner Corporation (09/2016)