As a Lead Data Scientist at CVS Health, I spearhead a high-impact research team dedicated to exploring the causal effects of telemedicine on patient outcomes, such as cost, quality of care, and recovery. Leveraging advanced statistical techniques, including Inverse Probability of Treatment Weighting (IPTW) and Targeted Maximum Likelihood Estimation (TMLE), we deliver data-driven insights that shape industry standards. Our work has been presented at leading industry conferences and is currently undergoing peer review for publication.I was instrumental in improving telemedicine accessibility by designing and executing comprehensive, data-driven marketing strategies from start to finish. Through the application of machine learning, I identified key predictors of virtual care utilization, designed targeted cohorts, and implemented comprehensive experimental designs. My efforts helped optimize campaign performance through over 50 A/B tests in email marketing within two years, doubling product engagement and conversion.Beyond marketing and campaign optimization, I contributed to demand forecasting for telemedicine services using time series modeling and led the development of an unsupervised classifier to better understand provider-patient networks. This work offered critical insights into the effects of local versus national providers on patient experiences and informed larger organizational modeling efforts.As a team leader, I manage two talented data scientists, fostering collaboration on diverse projects while solving complex business problems. My role extends to serving as a bridge between technical and non-technical teams, ensuring that model outputs are effectively translated into actionable business strategies. Additionally, I provide technical mentorship on statistics, coding, and machine learning methodologies, promoting excellence across the team.

I contributed to the development of oncology Value-Based Care (VBC) models and a range of other oncology-related products. Collaborating closely with product and sales teams, I helped translate complex analytical insights into actionable product improvements and compelling sales strategies.Using regression, bootstrapping, and machine learning techniques, I developed an oncology patient risk model that was pivotal for risk adjustment in VBC contract adjudications. This model was instrumental in ensuring accurate risk-adjusted patient outcomes, saving millions in potential erroneous payouts.Additionally, I created custom automated simulations that leveraged client-specific data to produce savings estimates, complete with confidence intervals. This complex project involved building sophisticated data preprocessing pipelines, optimizing sampling procedures, and delivering clear visualizations to help clients understand their savings potential.I also led a team of contractors in building a dashboard that reported key KPIs and savings related to a prior authorization program. This tool was used by product owners, senior executives, and external clients to track performance. Throughout this time, I mentored junior data scientists and an intern, guiding them through oncology prediction models, from disease complication to cost predictions.

I contributed to key product developments, including identifying target markets for infusion site-of-care savings products, resulting in an estimated $5M in savings. I also automated evaluation reports for client savings, which supported strategic decision-making for pilot rollouts in the specialty drug space.Working alongside subject matter experts, I developed clustering algorithms that informed risk-sharing contracts in oncology, ensuring more precise targeting for our cost-saving programs.

Working under Dr. Bryce Dietrich using text data (mostly Twitter data). In this role I am in a statistical and code consultant role. I complete analyses and report findings on strict timelines to aid grant-funded research projects.

Work with Dr. Elizabeth Menninga on a variety of projects, primarily focused on network analysis. In this role I am in a statistical and code consultant role. I complete analyses and report findings on strict timelines to aid grant-funded research projects.

Worked with Dr. William Reisinger, Dr. Marina Zalaznoya, and Dr. Wenfang Tang on their projected funded by the Minerva grant titled “Informal Economies, Societal Stability and Regime Resilience in China and Russia”. In this setting I took on a role of data quality control, data cleaning, and quantitative analyses to assist the resarch team.

In this semester-long appointment I was appointed the competitive position of collaboratory manager in the political science department. In this role I managed the computer lab of the department. Simpler responsibilities included maintaining and updating computers, preparing the department for guest speakers, and attending alumni board meetings. I also served as a teaching assistant for the statistical modeling courses in the political science department (both graduate and undergraduate levels). I also served as a statistical consultant for graduate students and faculty.

Worked with Brian Lai (Fall 2016, Spring 2017, Summer 2017, Spring 2018), William Reisinger (Fall 2017), and Elizabeth Menninga (Fall 2017) as a teaching assistant. In each role my responsibilities included leading sections, grading, and communicating with students.