Promoted to Staff Data Scientist on the Analytics team. Was responsible for large scale projects that tied together clinical outcomes with various other parts of the business.
● Key Accomplishments:
• Created a framework that measured what a typical clients care path would look like and utilized this to help inform the Lifetime Value for clients that were used in financial planning and modeling.
• Researched and created the company's clinical outcomes data to show client growth through actionable outcomes. – Model was able to predict and show that clients would show initial improvement by their 5th or 6th session and could measure for various groups when sustained improvement could be expected.
• Served as main research liaison for University of Denver research partnership – Curated research data sets that followed all IRB and privacy rules to be used by external research partners. – Contributed to 5 different published journal articles.

Joined the Data and Analytics team to specifically focus on developing and measuring clinical outcomes for clients and providers. Played a key role in demonstrating superior clinical effectiveness, which was an instrumental key factor for SonderMind securing $150 million in Series
● funding and reaching "unicorn" status.
Key Accomplishments:
• Developed five key company measures for demonstrating clinical effectiveness. – Developed a unique methodology to measure the clinical effectiveness of SonderMind's clients and their clinical improvement.
• Built a one of a kind index-based metric to measure the effectiveness of SonderMind therapists. – Allowed for the measurement of nine-plus different inputs individually against other therapists in order to understand who was providing the best quality care. PAGE 2 OF 2

Appointed to the Data Science to Patient Value (D2V) project as part of pursuing and publishing dissertation findings in journal articles. Used a variety of advanced analytical methods, such as machine learning, regression analysis, and natural language processing to design and implement predictive models that derived key insights for exploratory analysis and generate actionable insights.
Key Accomplishments:
• Created SQL queries and consulted for the State of Colorado's COVID-19 Modeling Team to report on state policy efficacy in terms of individual's mobility before and after the Stay at Home and Safer at Home orders.
• Developed an independent data science blog whose posts demonstrated web-scraping, natural language processing, and machine learning. – Building a surveillance tool utilizing Twitter data to identify future COVID-19 outbreaks using machine learning to classify over 400,000 tweets and then utilizing a real feed of new tweets to identify possible hotspots for infecti

Collaborated with multidisciplinary team of economists, epidemiologists, clinicians, and researchers in various research projects as a data expert, leading analysis and identifying research opportunities in each project. Cleaned, prepared, and maintained a variety of health-specific data sets prior to analysis. Removed data identifiers, created data dictionaries, and readied data for upload to public use archives.
● Key Accomplishments:
• Built analysis datasets for complex and disparate datasets (such as Colorado Medicaid) that contained >10M observations and required exploratory and primary analysis.
• Used fuzzy matching techniques to design complex data-wrangling programs in Stata and SAS to combine service specific data from Denver Health and Rocky Mountain Human Services.
• Generated a dataset by adapting PDF information for use in natural language processing and keyword analysis.
• Saved thousands of dollars and >6 months of work by innovating programmatic web-scraping tool

Lecturer of Economics (2012 – 2014) Adjunct Instructor (2011– 2012) Instructed students in Principles of Microeconomics and Principles of Macroeconomics.

Instructed students in Principles of Microeconomics and Principles of Macroeconomics.