Survey Research Scientist, Strategy and Insights, Reputation Marketing & Insights

This open role is for a Survey Research Scientist within the Global Communications and Community Impact (GCCI) org at Amazon. This role be responsible for conducting advanced modeling and inference on the attitudinal and perception-based survey data our team collects, as well as developing the mechanisms to ensure statistical rigor in our sampling and weighting practices.

This person will be responsible for proposing and executing statistical analysis and modeling techniques to surface data-driven insights to guide the broader communications organization. This person will partner with market researchers, media analysts, and strategists from across our team to dive deep into data, establish strategic territories, and identify audience opportunities. The work will challenge you to be resourceful, think big while staying connected to the details, translate advanced analytics into strategic direction, and embrace a high degree of change and ambiguity at speed. The scope and scale of what we strive to achieve is immense, but it is also meaningful and energizing.

Key job responsibilities
- Conduct advanced modeling and inference on survey-data (ex: factor analyses, structural equation modeling, hierarchical linear modeling, latent growth curve modeling, causal inference where possible, etc.)
- Develop experiments to improve survey design, data quality, and drive causal learnings
- Translate results from the advanced statistical modeling into digestible reports, tables, and graphs
- Draw from existing scientific literature and methods
- Mechanize sampling and weighting procedures for our survey projects
- Communicate data-driven insights and recommendations through papers and other written communications
- Scale learnings and insights to other teams in GCCI and the broader Amazon marketing and communications community

We are open to hiring candidates to work out of one of the following locations:

Seattle, WA, USA

Basic Qualifications

- PhD, or Master's degree and 4+ years of quantitative field research experience
- Knowledge of R and SQL
- Knowledge of quantitative approaches (e.g., t-tests, regressions, ANOVAs, etc.)
- 3+ years of investigating the feasibility of applying scientific principles and concepts to business problems and products experience
- Experience working with and analyzing survey and perception-tracking data

Preferred Qualifications

- Experience building web based dashboards using common frameworks
- Experience converting research studies into tangible real-world changes
- Experience with qualitative research methods
- Knowledge of AWS platforms such as S3, Glue, Athena, Sagemaker
- Experience in standard machine-learning and statistical modeling tools and techniques (e.g. random forests, gradient-boosted regression, LASSO, logistic regression)

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $124,100/year in our lowest geographic market up to $212,800/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. Applicants should apply via our internal or external career site.