I hold a 2019 PhD in Mathematical Sciences from Clemson University, with an emphasis in Statistics. My professional hobbies include coding, modeling, and analyzing complex datasets. Although I apply my current data science skills to solve real-world problems, I find enjoyment in staying current in academic literature. Often I am deriving and coding novel published techniques from scratch, mainly in machine learning models such as neural networks and their different frameworks like regular feed-forward, CNNs, and RNNs. Currently, I am deep diving into large language models, particularly transformers and their generative AI tasked GPT models. I am an avid Python coder, and my full tech stack can be found in my resume, along with my other professional work.

Location

Charlotte, NC

Employment

Data Scientist @ Teladoc Health	(Apr 2022 — Present)
Quantitative Operations Associate @ Bank of America	(Sep 2021 — Apr 2022)
Quantitative Finance Analyst @ Bank of America	(Oct 2019 — Sep 2021)

Resume

Socials

Updated: April 2024

Data Scientist, Teladoc Health

Senior Data Scientist (Feb 2024 — Present); Data Scientist III (Apr 2022 — Feb 2024)

Developing a content generation LLM using Azure's OpenAI REST API to personalize marketing communications based on internal member data. The model adheres to business constraints and allows marketing to tailor more than 25 different communications to identified member categories. This replaces a single generic communication and increases member engagement through relatable content, and is deployed through a CI/CD framework for seamless integration into marketing campaigns.
Developed a tree-based recommender model informed by internal and external data (CDC and epidemiological studies) which resulted in over 42% of the member base being targeted by additional relevant marketing outreaches. This model has also been integrated in other areas of the business and won the Teladoc Health Innovation Award for its proven impact on the business.
Developed an XGBoost model to score members based on their propensity to engage with the expert medical opinion service. The model was built on historical campaign data which resulted in more than 50% lift in conversion rates when targeting top deciles, aligning with the company's campaign optimization efforts in improving ROI metrics. The model is used for each quarterly expert medical opinion campaign.
Developed a Markov chain expected return time framework to guide timely communications for members who deviate from their normal account activity. This replaced a fixed 10-day inactivity flag with a model-based approach that dynamically estimates a member’s normal patterns. This model increased engagement rates by over 20% for members receiving the modeled communication compared to the fixed flag.
Developed several adhoc machine learning propensity and uplift models to continually score members based on their likelihood to respond to certain marketing channels or engage in various products. Model performance is measured through outcome driven metrics such as decile performance, accuracy, recall, or precision and is monitored quarterly to guide timely model retuning.
Built and conducted statistically sound experimental designs to gather member behavioral data. This insight is useful for recommending member personalization and engagement initiatives to marketing partners in an effort to increase member engagement and return on investment (ROI).
Contribute to and maintain a large scale data science GitHub repository which houses a suite of registered models, database connections, and databricks notebooks used organization wide.
Software: Python, PySpark, Databricks, GitHub, AWS, Delta Lake, SQL, etc.
Techniques: Decision trees, random forests, XGBoost, Markov chains, experimental design, A/B testing, etc.

Quantitative Operations Associate, Bank of America

Assistance Vice President (Sep 2021 — Apr 2022)

Developed quantitative volume forecasting models for the Bank'~s capacity and strategic workforce planning in the First Mortgage and Home Equity loans space.
Led the automation and standardization of volume forecast modeling to be used organization wide, which reduced model development time by over 50% and eliminated error-prone manual processing.
Collaborated with business leaders to enhance the forecasting of volumes based on historical trends and changes in business processes.
Presented quarterly volume forecasts to business executives and finance partners for approval.
Software: Python, SAS, GitHub, Excel, etc.
Techniques: ARIMA, ARIMAX, X13, etc.

Quantitative Finance Analyst, Bank of America

Assistance Vice President (Oct 2019 — Sep 2021)

Led the redevelopment of the defaults and ratings transition model, one of the largest and high-risk models for the Bank's commercial and industrial portfolio. The model outputs point-in-time quarterly transition matrices used for CCAR, CECL, and IFRS 9 reporting.
Developed, implemented, and documented various sensitivity tests to assess potential operational risks in the modeling framework. Testing involved derivation and implementation of the least absolute shrinkage and selection operator (LASSO) for variable selection and incorporating constrained maximum-likelihood estimation to increase model output granularity for more precise modeling.
Contributed to a large scale collaborative model code repository using version control software and wrote unit tests to ensure code changes adhere to expected output. This code base is used across the Bank's risk modeling organization.
Improved model accuracy and flexibility by incorporating industry specific macrovariables not previously considered in the modeling framework. This was a step forward in the Bank's efforts to climate risk modeling and allowing more accurate stress forecasts as industries are affected differently during stress.
Performed data assessment and variable selection using likelihood ratio test, Wald test, and backward stagewise selection. Model explanatory power was demonstrated through default, downgrade, and upgrade rate backtesting and various goodness-of-fit metrics such as AUC and RMSE.
Presented model forecasts to business stakeholders and communicated material model updates that drove changes in expected credit losses.
Accepted model hand-off halfway through model redevelopment for the probability of default (PD) component. The model is developed to be compliant with the Third Basel Accord (Basel III) for the Bank's risk weighted asset (RWA) and economic capital (EC) planning.
Documented the PD model's redevelopment and communicated the updates to business stakeholders and model validators for a successful validation.
Performed COVID-19 impact analysis to understand model implications and to defend the model was fit for use during the pandemic.
Software: Python, Linux, GitHub, SQL, Excel, etc.
Techniques: Cox proportional hazard rate model, maximum likelihood estimation, logistic regression, likelihood ratio test, variable selection, bootstrapping, goodness-of-fit metrics, etc.

Joyner, C., McMahan, C., Tebbs, J., and Bilder, C. (2024+). A multivariate Bayesian mixed effects model with variable selection for multiplex group testing data. In preparation.
Yusuf, I., Miskad, U., Lusikooy, R., Arsyad, A., Irwan, A., Mathew, G., Suriapranata, I., Kusuma, R., Pardamean, B., Kacamarga, M.,Budiarto, A., Cenggoro, T., Pardamean, C., McMahan, C., Joyner, C., and Baurley, J. (2021). Genetic risk factors for colorectal cancer in multiethnic Indonesians. Scientific Reports, 11, 9988.
Joyner, C., McMahan, C., Tebbs, J., and Bilder, C. (2020). From mixed-effects modeling to spike and slab variable selection: A Bayesian regression model for group testing data. Biometrics, 76, 913-923.
Sekhon, R., Joyner, C., Ackerman, A., McMahan, C., Cook, D., and Robertson, D. (2020). Stalk bending strength is strongly associated with maize stalk lodging incidence across multiple environments. Field Crops Research, 249.
Joyner, C., McMahan, C., Baurley, J., and Pardamean, B. (2020). A two-phase Bayesian methodology for the analysis of binary phenotypes in genome-wide association studies. Biometrical Journal, 62, 191-201.
McMahan, C., Baurley, J., Bridges, W., Joyner, C., Fitra Kacamarga, M., Lund, R., Pardamean, C., and Pardamean, B. (2017). A Bayesian hierarchical model for identifying significant polygenic effects while controlling for confounding and repeated measures. Statistical Applications in Genetics and Molecular Biology, 16, 407-419.