A successful candidate will be a self-starter, comfortable with ambiguity, able to think big and be creative, while still paying careful attention to detail. You will apply your econometrics expertise to identify opportunities for further research and to provide insights that drive larger initiatives. You should be able to translate how data represents the customer journey, be comfortable dealing with large and complex data sets, and have experience using machine/deep learning at scale to solve business problems. You should have strong analytical and communication skills, be able to work with product managers and software teams to define key business questions and work with the analytics team to solve them. You will join a highly collaborative and diverse working environment that will empower you to shape the future of Amazon marketing, as well allow you to be part of the large science community within the Customer Behavior Analytics (CBA) organization.
Key job responsibilities
The main responsibilities for this position include:
- Apply your expertise in causal modeling and ML to develop systems that describe how Amazon’s marketing campaigns impact customers’ actions
- Own the end-to-end development of novel causal inference models that address the most pressing needs of our business stakeholders and help guide their future actions
- Improve upon and simplify our existing solutions and frameworks
- Review and audit modeling processes and results from other economists/scientists, both junior and senior
- Work with marketing leadership to align our measurement plan with business strategy
- Formalize assumptions about how our models are expected to behave and explain why they are reasonable
- Identify new opportunities that are suggested by the data insights
- Bring a department-wide perspective into decision making
- Develop and document scientific research to be shared with the greater science community at Amazon
About the team
The Customer Behavior Analytics (CBA) organization owns Amazon’s insights pipeline, from data collection to deep analytics. We aspire to be the place where Amazon teams come for answers, a trusted source for data and insights that empower our systems and business leaders to make better decisions. Our outputs shape Amazon product and marketing teams’ decisions and thus how Amazon customers see, use, and value their experience.
BASIC QUALIFICATIONS
- PhD in economics or equivalent
PREFERRED QUALIFICATIONS
- 2+ years of industry, consulting, government, or academic research experience
- Knowledge of at least one statistical software package such as R, Stata, Matlab, SAS
- Experience in implementing modern machine-learning methods (e.g., boosted regression trees, random forests, neural networks)
- Experience working with very large, disparate data sets and big data tools such as Spark
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 $136,000/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. This position will remain posted until filled. Applicants should apply via our internal or external career site.