Matthew Nelson, PhD

Research Scientist

mattnelsonphd@gmail.com · (727) 771-5881 · www.mattnelsonphd.com · linkedin.com/in/mattnelsonphd

Research scientist specializing in survey methodology, statistical modeling, and quantitative research. I design the study, run the analysis, and write the brief. Corporate reputation and academic political science, from Fortune 50 strategy to research cited in a U.S. Supreme Court amicus brief.

EXPERIENCE

Amazon — Reputation Marketing & Insights

• Conduct global survey research and advanced analytics across Amazon Retail and AWS, measuring brand reputation, executive favorability, competitive positioning, and perceptions of Amazon as an AI leader
• Develop methodology across countries: sampling plans, quota frameworks, questionnaire design, fieldwork specifications, and weighting to census benchmarks, so results compare across markets and over time
• Model reputation outcomes using logistic regression, Shapley decomposition, and specification curve analysis to identify drivers of brand perception
• Build research infrastructure: automated Python pipelines and dashboards that turn raw fieldwork into crosstabs and tracking views
• Build an internal report-writing tool on the Claude API, with custom steering files for drafting briefs and checking numbers
• Interview research and engineering candidates; mentor and evaluate a research scientist intern

Purple Strategies

• Designed survey instruments and statistical models for Fortune 50 clients across energy, pharmaceuticals, consumer products, manufacturing, and technology
• Modeled reputation drivers with logistic regression and built driver maps ranking each attribute by predictive strength and brand association
• Ran multi-wave message and creative testing across distinct stakeholder audiences, combining max-diff with dial-tested focus groups
• Produced research briefs and executive presentations that turned quantitative findings into strategic recommendations
• Led firm-wide training on quantitative methods and AI tooling, and helped develop the AI policy governing data safety and researcher accountability

University of Southern California — Schwarzenegger Institute / Fair Maps Lab

• Published peer-reviewed research in PS: Political Science & Politics; cited in U.S. Supreme Court amicus brief, Moore v. Harper
• Applied regression analysis, ecological inference, and racially polarized voting methods to guide redistricting in Riverside County
• Co-authored gerrymandering report covered by the Washington Post, FiveThirtyEight, and CalMatters

SELECTED PUBLICATIONS

"Independent Redistricting Commissions Are Associated With More Competitive Elections."

PS: Political Science & Politics, 2023. Logistic regression with fixed effects and clustered standard errors on congressional election data, 1982–2018. Cited in U.S. Supreme Court amicus brief, Moore v. Harper.

"The Worst Partisan Gerrymanders in U.S. State Legislatures."

USC Schwarzenegger Institute, 2019. Found 59 million Americans living under minority rule in their state legislatures. Covered by Washington Post, FiveThirtyEight, CalMatters.

"Racially Polarized Voting in Riverside County."

County of Riverside, 2021. Ecological inference and homogeneous precinct analysis on precinct-level returns.

EDUCATION

SKILLS

Research Design: Survey methodology, sampling and quota frameworks, weighting and post-stratification, experimental design, causal inference, questionnaire development

Statistical Analysis: Logistic regression, Shapley decomposition, specification curve analysis, factor analysis, predictive modeling, structural equation modeling, ecological inference

Deliverables: Research briefs, executive presentations, crosstab reporting, data visualization

AI Tools: Claude API integrations with custom steering files, LLM-assisted analysis and coding, AI research governance

Programming: R (expert) · Python (advanced) · SQL · HTML/CSS

Projects: Bouncy Doggo, an App Store and web game built in HTML and JavaScript, balanced with simulation and statistical methods

Languages: English (native) · Spanish (fluent)