Developed an investment propensity model that enhanced campaign targeting, resulting in a notable 22% increase in assets under management (AUM) invested
Designed a customer lifetime value (CLV) metric that captured both current and potential customer value
Engineered a recommender system that uses customer profiles, life stage, and financial needs to recommend tailored investment funds
Optum Labs (UnitedHealth Group R&D)
Data Scientist
January 2020 -- April 2022
Achievements:
Created reusable data analysis tools automating data cleaning, feature selection, feature engineering, analysis, and report generation
Developed internal SQL and report generation packages (in Python) complete with documentation and unit testing
Conducted analyses and developed models for health focus areas
BPI-UHG Data Science (BUDS) Training Program
Data Science Apprentice
July -- December 2019
Participated in a 6-month intensive data science training program that taught both theoretical foundations and practical applications of data science in healthcare and finance
Statistics and ML: random forests, gradient boosting, neural networks, dimensionality reduction (PCA), power analysis, generalized linear models, SVM, propensity score matching, structural causal models, etc.
Deep Learning: familiar with state-of-the-art models for image and natural language processing, e.g., AlexNet, VGG, ResNet, DenseNet, ELMo, BERT, T5, GPT, etc.