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Senior LLM Scientist/ Tech Lead at Amazon
PhD graduate in statistics. Now building and deploying LLM powered chatbots for Amazon customer service. Specialties: LLM/Generative Deep Learning: Chatbot, Text Understanding, Automatic Summarization, Classification and Topic Modeling, sentiment analysis. Statistics: Regression, Causal Inference, Downstream Impact (DSI) Analysis, Statistical Learning, Classification, Predictive Analytics, Big Data, Information Retrieval, Time Series, High Dimensional. Tools & Technologies: Python, Spark, SQL, R, Unix & Linux.
University of Washington
Doctor of Philosophy (PhD), Biostatistics
January 1, 2012 – January 1, 2017
The University of Texas at El Paso
Bachelor’s Degree, Math & Statistics
January 1, 2008 – January 1, 2012
Amazon
Senior Applied Scientist/Tech Lead, LLM
March 1, 2023 – Present
Amazon
Machine Learning Scientist II
June 1, 2019 – July 1, 2023
Amazon
Machine Learning Scientist
December 1, 2017 – June 1, 2019
Amazon
Machine Learning Scientist Intern
June 1, 2016 – September 1, 2016
Greater Seattle Area
University of Washington
Teaching Assistant
September 1, 2014 – May 1, 2017
Seattle, Washington, United States
Fred Hutchinson Cancer Research Center
Research Assistant
September 1, 2013 – December 1, 2017
Greater Seattle Area
University of Washington
Ph.D. student
July 1, 2012 – June 1, 2017
Seattle, Washington, United States
Amazon Customer Service Artificial Intelligence Chatbot
June 1, 2016 – September 1, 2016
Involved in building an A.I. chatbot to answer questions and solve issues for customers by applying natural language processing, natural language understanding and machine learning
Amazon Fraud Detection for Customer Service Contacts
June 1, 2016 – September 1, 2016
• Identified/Created features that can be useful for predicting fraud • Built a machine learning model to predict the risk of fraud for incoming customer service phone contacts • Implemented the model in production and designed an alerting system for agents to prevent fraud
Evaluation of Outcome Improvement From Anesthetic Change
May 1, 2014 – October 1, 2014
For children undergoing tonsillectomy operations at the Bellevue Surgery Center, anesthesia providers changed their regimen from morphine to IV acetaminophen and fentanyl in 2013. We evaluated the effectiveness of different anesthetic regimens on pain control and provided formal reports on statistical results.
Statistical Analysis of Phase-III Vaccine Efficacy Trials
September 1, 2013 – May 1, 2016
This project is collaborated with Sanofi Pasteur to perform statistical analysis on two Phase III Dengue Vaccine Efficacy Trials: CYD14 (Asia) and CYD15 (Latin America). We evaluated the relationship between occurrence of Dengue cases and the level of neutralizing antibody titer 28 days after 3rd injection, and built novel causal inference algorithms for causal effect identification and vaccine efficacy modeling. Further, we developed innovative simulation methods as a superior alternative to bootstrap and machine learning techniques to extract information from complex datasets. This is a highly interdisciplinary project and we worked closely with clinicians and industry experts to deliver re-producible results and actionable recommendations. Publication to appear in New England Journal of Medicine.
Cultural Fit Analysis
The candidate has a strong background in research and applied science, primarily within Amazon and academic institutions. The projects demonstrate a focus on complex problem-solving and innovation, which aligns well with a data-driven culture. However, the project descriptions do not explicitly detail collaboration or team dynamics, making a full assessment of cultural fit challenging. The projects are diverse in application (healthcare, customer service, fraud detection) but all within a highly technical, analytical domain.
Soft Skills & Operational Fit
The candidate's experience as a Tech Lead and involvement in interdisciplinary projects suggests strong leadership, problem-solving, and collaboration skills. The description of implementing models in production and designing alerting systems indicates a practical, results-oriented approach. However, specific details on communication and teamwork within project descriptions are limited.