Research Scientist, Selling Partner Experience

Amazon.com Services LLC•4h ago

United StatesRemote$125.5K–$169.8KFull-timeMid Level3+ yrs exp

H-1B verified · 2310 LCAs

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Research ScientistData Scientist

We’re looking for a Research Scientist to join a team that measures and explains how over 2.4 million sellers and vendors experience selling on Amazon. You’ll apply survey science, psychometrics
applied statistics to help drive meaningful change at Amazon on behalf of Sellers. In this role, you’ll work across a variety of research methodologies to optimize our data collection, create scalable analytical approaches
deep dive the Seller experience to create rigorous, quantitative insights that senior leaders use to set strategy. Key job responsibilities Key Job Responsibilities - Apply psychometric and survey methodology techniques (e.g., IRT, factor analysis, scale development, single-item indicators) to measure seller experience constructs with scientific rigor - Contribute to frameworks that link seller attitudinal data to behavioral outcomes and identify high-impact opportunity areas - Design and execute statistical analyses including regression modeling, significance testing
driver analysis to identify what matters most to sellers - Apply observational causal evaluation methods to estimate the effects of policy changes, product launches
platform interventions on seller experience - Build and maintain analytical pipelines that transform raw survey data into production-ready metrics, reports
dashboards - Analyze open-ended survey responses using text classification, thematic coding
natural language processing techniques - Monitor and improve survey response rates, sampling methodology
data quality - Productionalize research code: take analyses from prototype to automated, reproducible pipelines that run reliably in production environments - Communicate findings clearly to technical and non-technical audiences through written reports, data visualizations
presentations - Collaborate with cross-functional partners to translate business questions into well-defined research problems and scientific metrics - Document research methods, assumptions
limitations transparently to ensure reproducibility A day in the life Your day typically starts with the data. You might spend the morning reviewing satisfaction trends, investigating a shift in a key metric
pulling together an analysis that explains what’s driving it. You’ll regularly meet with external teams to help them understand how a proposed product will affect seller sentiment and what the data says they should prioritize. You’ll also spend time in R or Python building, training
testing models to improve how we measure and act on sentiment data. About the team Our team owns the research and measurement infrastructure that tracks satisfaction across all 2.1 million selling partners on Amazon, spanning Seller Central, Next Gen Selling
Mobile. We sit at the intersection of data and strategy, partnering with teams across product, design
engineering to advocate for seller experience improvements. This is a high-visibility team where the work is consequential, the stakeholders are senior
the problems are genuinely hard.
PhD in a quantitative field
MS degree and 3+ years of quantitative field research experience - Experience investigating the feasibility of applying scientific principles and concepts to business problems and products - Experience with data analysis package (R, SAS, Matlab, etc.) - Experience using SQL databases to manage and analyze large data sets - Experience with lab-based user testing, remote testing, iterative prototype testing, survey design
usage of multiple methods within a study - Experience applying basic statistical methods (e.g. regression) to difficult business problems - Experience using data visualization tools - Experience with survey research methodology and psychometric measurement (e.g., item response theory, factor analysis, scale construction, reliability analysis) as well as single-item indicators
Experience in causal modeling like graphical models, causal Bayesian network, potential outcomes, A/B testing, experiments, quasi-experiments
data science workflows - Experience with any programming language such as Python, Java, C++ - Knowledge of machine learning processing: computer vision, NLU, NLP or operations research - Experience in performing regression analysis and building classification models using machine learning algorithms - Experience with various types of research methodologies is key, including quant, qual, 1P & 3P data, trend analysis & forecasting, etc. - Experience with AWS services including S3, Redshift, Sagemaker, EMR, Kinesis, Lambda
EC2 Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability
other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications
location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off
parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits . USA, WA, Seattle - 125,500.00 - 169,800.00 USD annually

Required skills

PythonRSQLAWSSASMatlabmachine learningNLPdata visualizationpsychometricssurvey researchregressioncausal modelingdata analysisstatistical analysis