Ongoing data science consultant to ticket broker firm providing data structure builds and analysis on purchasesData Engineering- Designing SQL database schema that reflects RDBMS principles and implementing ETL process to map historical purchase data into schema.• Successfully migrated historical data from dozens of excel sheets into comprehensive RDBMS with multiple tables in 3rd normal form.- Researching and creating API calls to capture, process and store real-time data• Successfully constructing automatic ETL process using SQL and API calls to capture, process, and store real-time data. Data Science- Creating KPI metrics combining processes like purchases, sales, and available credit.• Decisions utilizing KPIs have created thousands of dollars of value in increased profits and savings.• Current projects include: cleaning all historical purchase data, building dashboards for dynamic aggregations of KPI metrics, making statistical comparisons to answer questions about historical revenue patterns using Python data stack

Ongoing data science consultant to assistant political science professor modeling generational economic effects of Civil War experiencesCollaborating on research design, data structures, cost functions, and model selection metrics for overall research agendaNatural Language Processing- parsing sentences from naval ship records with a unique NER model using spaCy• Starting from a 40% accuracy test metric, achieved over 95% precision and recall test metrics.Classification- Building classification model estimating likelihood of an individual naval ship achieving bonus earnings using Python data stack and individual algorithm libraries • Quintupled F1 test metric over baseline model using LightGBMCurrent projects include: building regression model estimating individual sailors' share of naval ship bonus earnings based on demographic and Civil War experience data using Python data stack and regression model libraries

Writing curriculum content for machine learning, AI, statistical testing, and data analysis topics; giving lessons to students involving data science concepts and packages in the Python data science stack; and project managing curriculum updates at the data science program of Flatiron School's Chicago campusMajor accomplishments in selected areas:Data science- Created a custom unit-testing class used to test student answers for daily quizzes on machine learning, AI, statistical testing, and data analysis topics using packages in the Python data science stack like NumPy, SciPy, Keras, TensorFlow, Scikit-learn and Pandas• Reduced time required to review student work by a factor of 10• Unit-testing class adopted across Flatiron campuses - Created webinar discussing recommendation systems and the Surprise Python package for a marketing event geared toward prospective students and the general public• Multiple talks attended by hundreds of people- Created github repos containing hour-long walkthroughs of Python code demonstrating machine learning, AI, statistical testing, and data analysis concepts for every day of the 12 weeks of instruction• Adopted across Flatiron campuses Project management- Led a team that conceptualized, created, and deployed a pilot program of over 50 hrs of stat testing, data visualization, and Python content to give students additional practice beyond current curriculum• Worked with both Senior departmental managers and C-suite executives in conceptualizing and developing pilot• Pilot program launched for the August cohort- Led the Chicago campus data science team in the transition to remote learning due to COVID-19 • Team created procedures, content, and communication infrastructure both internally and student-facing• Scoped-out above with 48-hr notice of going remote, in the middle of a cohort's 12-week curriculum • Maintained above-average student feedback scores on instructional team performance

Applying data science concepts to 5 full-scale supervised and unsupervised machine learning projects requiring data collection, cleaning, analysis, modeling, visualization, and communication – primarily using Python tools.Successfully completed the ACCET-accredited 12-week immersive program.

Data scientist and analyst for a health insurance consulting firm. Responsibilities included establishing, maintaining and updating projection methodologies and automatic data flow processes for a data product, and establishing projection processes for new data products.Major accomplishments include: - Created projection methodologies of national health insurance membership across all lines of insurance that were adopted internally by third-largest health insurance provider. Introduced automatic data flow processes that reduced report production time from 3+ months with 5 analysts to 2 weeks with 3 analysts. Demoed capabilities of report production to potential clients. - Developed, implemented and productized a set of projection methodologies for year-to-year membership that incorporated membership, plan, claims, and call center data that was sold to multiple clients. Languages used include Python data stack, R and SQL.

Data Science consultant for Chicago-based healthtech startup Purple BinderProvide data analysis insight about client use of firm’s core product: a searchable database of health care providersProvide on-demand descriptive and statistical analysis, develop key indicators and models to track and predict product use, and explore data on my own initiative to assist co-founders in developing product and shaping the direction of the firmProgramming languages used: Python to acquire / clean data and visualize results, R for statistical analysis

Maintained document database in SQL. Performed search queries and created tables regularly and upon request.Provided legal support to partner and associate attorneys, including: accessing federal, state and private databases for relevant legal material, proofreading briefs for logical reasoning, researching precedents, drafting legal documents, analyzing statutes for city and state rules, researching business documents to determine corporate structure and history Provided trial support for three trials, including: preparing and maintaining the on-site trial office, providing details of evidence for attorneys while in court, and managing equipment and flow of witnesses

Summarized research paper contributions for inclusion to NORC's data enclave. Analyzed characteristics of researchers choosing to participate to aid data enclave outreach efforts.As part of intern group project, individually analyzed survey data of field interviewers conducting the Federal Reserve Board's Survey of Consumer Finances. Presented findings to Senior Microeconomist of the FRB.