Work Experience

Data Scientist — Elevance Health, Inc. (formerly Anthem Inc.)

March 2021 – Present  ·  Chicago, IL

I've been building and owning production ML and LLM systems to automate clinical prior authorization — a process that traditionally requires physicians and nurses to manually review patient records against clinical guidelines. My work spans 30+ medical guidelines and multiple business units, and has collectively saved tens of thousands of clinical hours annually.

A large part of my recent focus has been on large language models. I designed a DAG-based LLM pipeline for therapy visit authorization with swappable processing nodes, Pydantic-enforced structured outputs, and robust retry logic — achieving 90% accuracy and generating around $200K in monthly savings. I also built a hybrid RAG system combining BM25 sparse retrieval with OpenAI dense embeddings via Qdrant, plus cross-encoder reranking, which cut context token usage by 40% while improving clinical document search quality.

Alongside the LLM work, I've trained and maintained LightGBM models across 15+ guidelines, tuning decision thresholds against target false-positive rates and applying SHAP values for clinical explainability — enabling 30% of cases to be automatically approved and saving roughly $100K per month. This work eventually led to a US patent (US 12293835 B1) for a novel AI-based authorization automation system. I also built clinical NLP pipelines using UmlsBERT and CODER embeddings with spaCy and Stanza for named entity recognition, extracting structured medical facts from free-text clinical notes at 85% precision.

On the engineering and team side, I contributed to the MLOps foundation by building pip-installable internal packages with UV and Poetry, integrating GitLab CI/CD pipelines for linting, testing, and deployment, and cutting release cycle time by 50%. I also led a pod of six data scientists, architecting a scalable, reusable codebase that could be rapidly adapted across new clinical guidelines — driving over 25 model deployments in five months.

Machine Learning Consultant — Hamiltonian Systems, Inc.

June 2020 – December 2020  ·  Pittsburgh, PA

I built machine learning models to predict weekly transaction quantities and annual transaction occurrences, using Keras and TensorFlow to construct neural networks trained on historical usage data, achieving over 70% accuracy. I also led a team of five to build inventory lifecycle prediction models using Random Forest and XGBoost on 440K+ transactions, reducing forecast error by 22% over baseline.

After the models were ready, I designed a multithreaded Flask API to orchestrate ML model training and inference pipelines with async task management and cloud deployment via REST endpoints — reducing manual engineering workload by 90% and making the entire pipeline accessible with a single button click.

Research Assistant — Heinz College, Carnegie Mellon University

March 2020 – May 2020  ·  Pittsburgh, PA

As a Research Assistant at Carnegie Mellon University, I collaborated with David Wang on Hope Hurts: How High Expectations on Special Days Disappoint Yelp Users (SSRN). The research investigated how elevated expectations around special occasions — birthdays, anniversaries, holidays — shape the way people experience and review restaurants.

To answer that question, I analyzed 163,000 Yelp reviews using Structural Topic Modeling in R to uncover latent sentiment patterns and topic clusters. I then applied regression analysis alongside Propensity Score Matching to control for selection bias and estimate the effect of special-day dining on user satisfaction scores.

Consultant Intern — Deloitte Consulting

November 2018 – May 2019  ·  Shanghai, China

Data Analyst Intern — Dell Technologies

July 2018 – October 2018  ·  Shanghai, China

Business Development Intern — Infralutions, Inc.

January 2018 – February 2018  ·  Hong Kong, China (remote)