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I am a research scientist specializing in causal inference, experimental design, and quantitative analysis to drive data-informed decisions for policy, health products, and digital platforms. I have experience leading large-scale studies, building predictive models, and translating complex data into actionable insights for diverse stakeholders. I collaborate with clinicians, researchers, and developers to improve data quality, accelerate delivery, and design user-centered interventions.…I am a research scientist specializing in causal inference, experimental design, and quantitative analysis to drive data-informed decisions for policy, health products, and digital platforms. I have experience leading large-scale studies, building predictive models, and translating complex data into actionable insights for diverse stakeholders. I collaborate with clinicians, researchers, and developers to improve data quality, accelerate delivery, and design user-centered interventions.

Amy Yunyu Chiang

Data Scientist, Data Analyst, Programmer, +2

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I am a research scientist specializing in causal inference, experimental design, and quantitative analysis to drive data-informed decisions for policy, health products, and digital platforms. I have experience leading large-scale studies, building predictive models, and translating complex data into actionable insights for diverse stakeholders. I collaborate with clinicians, researchers, and developers to improve data quality, accelerate delivery, and design user-centered interventions.…I am a research scientist specializing in causal inference, experimental design, and quantitative analysis to drive data-informed decisions for policy, health products, and digital platforms. I have experience leading large-scale studies, building predictive models, and translating complex data into actionable insights for diverse stakeholders. I collaborate with clinicians, researchers, and developers to improve data quality, accelerate delivery, and design user-centered interventions.

Available to hire

I am a research scientist specializing in causal inference, experimental design, and quantitative analysis to drive data-informed decisions for policy, health products, and digital platforms.

I have experience leading large-scale studies, building predictive models, and translating complex data into actionable insights for diverse stakeholders. I collaborate with clinicians, researchers, and developers to improve data quality, accelerate delivery, and design user-centered interventions.

Skills

Python

Data Visualization

SQL

Experience Level

Python

Expert

Data Visualization

Expert

SQL

Expert

Language

English

Fluent

Work Experience

Consultant at DREAMS Center for Diabetes Translational Research

July 1, 2024 - Present

Design and execute data-driven strategies to generate actionable insights for diabetes interventions, partnering with cross-institutional stakeholders (Stanford, UCSF, Kaiser) to advance health equity and inform scalable solutions.

Affiliated Research Scholar at Computational Social Dynamics Lab

December 1, 2021 - Present

Applied computational methods to analyze 108K+ Facebook posts and 1.4K BLM protests, uncovering patterns in social movements and media framing that informed scalable insights for digital engagement strategies.

Research Scientist at University of California, San Francisco

June 1, 2021 - Present

Led research evaluating social and economic policies on health outcomes and safety net programs; produced 5 publications (4 first author) and actionable insights for NIH/NIMH ($700K) and AHRQ ($1M) projects. Served as analytics lead—providing statistical guidance, dashboards, and training to interdisciplinary teams while mentoring junior analysts to improve data quality and accelerate delivery. Developed interactive R/Shiny apps and novel sequence analyses to visualize COVID-19 policy impacts; optimized trials and usability studies through collaboration with clinicians, researchers, and developers. Analyzed patient EHR data to evaluate treatment protocols, uncovering barriers to uptake and informing design of more user-centered health interventions.

Doctoral Researcher/Teaching Fellow at University of Pittsburgh

September 1, 2015 - October 1, 2021

Designed survey experiments (N=583) to optimize user engagement and inform policy design; managed 100+ undergraduates per semester in quantitative methods (R); awarded 2022 Will Moore Prize for best solo-authored publication; applied principal component analysis and analyzed survey data to identify behavioral patterns, informing predictive models and actionable recommendations.

Research Assistant at Taipei Forum Foundation

December 1, 2013 - August 1, 2015

Synthesized multi-source insights to produce policy briefs and actionable recommendations on U.S.-Taiwan relations; planned and executed forums and workshops, enhancing stakeholder engagement and enabling evidence-based decision-making.