• Designed and productionized marketing machine learning model for a client, increasing prospect engagement by over 50%.• Developed insurance profitability allocation engines underlying modern analytics platform, the latter of which was estimated as enabling $20 million in additional annual revenue.• Developed graph database for helping to inform decision-making related to staffing.• Helped facilitate machine learning maturity assessment, which identified over 50 value-added use cases across the firm. Additionally designed ML infrastructure demos for use, within the Azure environment.• Helped facilitate machine learning assessment for brokerage firm, providing recommendations concerning automated processing of service request emails.• Served as lead data scientist for healthcare firm: guiding, mentoring, and aiding six data analysts, while delivering research products, custom reports, and recommendations for best practices.• As data engineer, helped to implement medallion architecture on a Databricks platform for supply chain firm.

• Served as product owner, primary SME, and analytical support lead for five analytical models, which offered over $15 million (est.) in annualized cost savings for operations. • Provided data science consulting in customer relationship management space, generating insights from wrangling in big data environments (Teradata, Hive databases) and Python analytics tools (pandas, numpy, matplotlib) to identify over $1 million in cost saving opportunities.

• Facilitated (re)design of multiple features of SAS-based, recommendation-producing applications - including conceptual blueprinting, forecasting with using time series and generalized linear models, troubleshooting (24/7 service), and QA testing – within an Agile framework. Worked independently, learned quickly as needed, and consistently facilitated communication to stakeholders on a timely, as needed, basis.• Assisted exploratory efforts to leverage natural language processing, and facilitate topic modelling, using unsupervised ML algorithms (Latent Dirichlet Allocation, clustering) in Python, to identify and discover patterns within unstructured, speech-to-text data.

Analyzed results from survey and test data, and reported summaries of these evaluations.

Oversaw the acquisition, processing and analysis of confidential, administrative level datasets.

Identified datasets for modeling biomass adjusted productivity, for U.S. fisheries. Additionally co-authored paper chronicling the historical application of Optimum Yield in U.S. fishery management.

Research Assistant:Researched under supervision of faculty members, offering help and consultation as needed, while working with a variety of datasets and statistical programs. Topics researched include issues pertaining to environmental, resource, energy, and land use economics.Teaching Assistant:Attended undergraduate course lectures; facilitated weekly office hours; graded assignments, examinations, and projects; organized weekly discussion sessions; and filled in as substitute lecturer.

Led three review sessions per week for students in Principles of Economics courses, fielding questions and preparing material covering main concepts from preceding lectures. Duties involved learning how to work with students with different styles of thinking and learning.

Researched, collected, and organized economic data; assembled information for publication in presentations and published articles; learned about local economic indicators.

Researched company and industry profiles, provided input for reports.