• Worked in an agile environment of four data engineers and a senior manager to perform ETL testing with SQL and Python.• Engineered a proof-of-concept NLP data pipeline to perform deduplication on 20M+ redshift records.• Performed pre-migration ETL data quality analysis with pandas and SQL to identify duplicate records and associated causes in Redshift and MySQL.• Configured low-latency Python code to decrease runtimes by ~25% for 1M+ records with cProfile.

• Translated Packt’s authoring request for the title ‘Machine learning with AWS’ to both a book about natural language processing (NLP) algorithms and a Python-based NLP data pipeline.• Architected a NLP data pipeline in Python with S3, Lambda, and Comprehend to output sentiment, key phrases, and entities from text documents. • Developed 40+ Python programming examples to augment NLP concepts including topic modeling, latent dirichlet allocation, theme extraction, sentiment analysis, and entity detection. Course URLs:• Amazon: https://www.amazon.com/gp/product/1789806194• Packt: https://www.packtpub.com/product/machine-learning-with-aws/9781789806199

• Enabled a client to meet a 72-hour lead generation deadline by web-scraping a JavaScript-rendered website with Python. • Designed and implemented a Python based web crawler using Selenium to automate sign-in and data extraction. • Privately contracted by a VP of Physician Development to programmatically identify dermatology practices for acquisition.• Programmed a Python algorithm to integrate disparate lead generation data from multiple open source APIs.

• Created a valuation model from multiple data sources (e.g. medical records, claims, and tax forms) to identify risk factors in M&A deals of independent practice-owning physicians.• Transformed disparate lead generation data (e.g. CSV, JSON, HTML, and XML) with a custom ETL into a PostgreSQL data warehouse for lead generation analysis.• Programmed a ranking algorithm on 2M+ records to prioritize lead generation, which resulted in a 10.0% prospect response and participation at M&A events.• Resolved a partial 500,000+ row csv with Python and an open source API to prioritize potential prospects. • Prepared M&A practice valuation reports (each deal $200,000+) by analyzing: financial documents, insurance ratios, procedure ratios, and patient demographics.

• Performed participatory analysis for community action and assessed community needs by priority. • Implemented a 100% community-funded medical incinerator with local Ministry of Health department leaders collaboration that benefited 7,000+ people.• Facilitated successful health education events with Ministry of Health professionals that increased community awareness to health education topics.

• Analyzed data for a clinical Pediatric Anxiety study in collaboration with the Children's Hospital of Illinois at OSF Saint Francis Medical Center.• Taught introductory statistics to undergraduates.