Leverages artificial intelligence to improve operational efficiency and enhance patient care

• Led Data Science initiatives and data science product development which included external app user-facing and internal business user-facing models • Led a mission critical database migration from Google Big Query and MySQL on AWS and merged it all into a single data warehouse on AWS Redshift to achieve long term data maturity for the organization and ensure best practices for data governance • Managed, mentored, and inspired a team of 2 data scientists and 1 data architect and managed a $1M budget for data science • Built many of the data science processes and operations from scratch including Agile and Kanban project management and Confluence documentation of architecture, SOPs, training, knowledge sharing, and roadmaps • Cleaned up the data warehouse by organizing dirty, disjointed datasets and building data engineering processes for transforming them into useable tables to be consumed by several business users • Managed the Data Tracking Plan and data dictionary and the development of new databases and tables • Collaborated with the Product team including Engineering, Product Design, and Product Managers for the successful deployment of machine learning models • Built and deployed several data science products in Google Cloud and Databricks including a Content Recommender, Grants Scraper RPA Bot, k-Means clustering Personas model, text classification/named entity recognition/sentiment analysis of customer support tickets, and Recency-Frequency-Tenure segmentation of users for prioritization of customer support tickets • Managed the Customer Data Platform (Twilio Segment) and integrated several 3rd party data sources into the data warehouse including ZenDesk, Hubspot, MailChimp, and Salesforce • Managed 4 cloud software vendor relationships and explored new technologies to ensure Hello Alice data science operations can scale with new user growth and data volume.

● Second analytics and data science hire in the company’s history and helped lead the development of the analytics and data science team from the ground up by deploying machine learning models into the day-to-day marketing and business operations and recruiting, hiring, growing, and developing our team to 6 FTEs. ● Managed, led, mentored, and inspired a team of 3 analysts/data scientists and conducted their bi-annual performance reviews ● Conducted training with direct reports, shared knowledge and business philosophies, and prioritized their projects ● Optimized direct mail campaigns by building scalable machine learning propensity models that saved $1,434,637 in print costs over a 3-year period while still retaining highest likelihood responders. ● Developed, tested, and deployed Churn Prediction and Market Basket Analysis models using R and Microsoft Azure Machine Learning ● Developed and deployed 3 ML product recommender algorithms (Next Best Product, Frequently Bought Together, and Cross Category) for use in customer lifecycle emails, variable print campaigns, and product description pages on the website that generated $2,501,401 in incremental revenue since deployment. ● Created automated process for image recognition and descriptions, and metadata and keyword tagging for hundreds of internal image assets for SEO team using R, Python, and Microsoft Cognitive Services ● Developed an NLP model that scraped and “read” 2,635 magazine articles on company’s website and mapped statistically relevant keywords from the article to a Universal Health Topic tag for each URL. NLP model built using Scrapy in Python and udpipe in R. ● Developed an automated anomaly detection model for 50+ key metrics that sent anomaly alerts to Slack ● Developed an automated SEO keyword research bot that does keyword research on a batch of keywords uploaded by user that leverages the SEMRush API and reduced a 10+ hour manual research process to 48 seconds