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Economist, Causal Inference, SimEx, SCOT

Job ID: 2069635 | Amazon.com Services LLC

DESCRIPTION

Job summary
Are you seeking an environment where you can drive innovation? Do you want to apply inference, advanced statistical modeling and causal inference techniques to solve world's most challenging problems for Amazon's worldwide supply chain that drives its e-commerce engine? Do you want to play a crucial role in the future of Amazon's Retail business? Do you want to be a part of a journey that develops a new technology from scratch for answering critical business question in Amazon Retail?

Every time an Amazon customer makes a purchase, a number of systems are involved: these systems help optimize acquisition, enable a number of purchase options, ensure great , store products so they are available for fast delivery, and minimize package frustration. The Technology (SCOT) Group develops and manages these systems. We are central to Amazon customers' ability to find what they want and get it when they want it.

The Observational Data Causal Inference team tackles some of our hardest causal inference questions for Amazon worldwide supply chain, feeding results back to our stakeholders to continue to drive innovation in our supply chain technology to better serve our customers. Amazon's supply chain is extremely complex and operates at massive scale, thereby making it an attractive playground for causal inference research at scale where the results have immediate and tangible impact on our customers. This is a brand new team that sits alongside the established sister team (Inventory Planning and Control Lab) that runs randomized experiments for Amazon's worldwide supply chain.

This position is open to the following locations: Seattle and virtual.


Key job responsibilities
* Lead the development of a consistent, integrated framework for assessing casual relationships the multitude of upstream and downstream supply chain metrics
* Interact with senior leaders in Amazon supply chain, both technical and non-technical, to understand their primary use cases for causal inference
* Independently write technical and business documents to communicate ideas and proposals to various audiences
* Incorporate new data sources and creative methodology innovations to improve model performance
* Mentor junior teammates to improve their understanding and application of science to causal and structural economic problems
* Work alongside and collaborate with our randomized experimentation team (IPC Lab) to further drive understanding and innovation in supply chain technology

BASIC QUALIFICATIONS

  • PhD in Economics or closely related field

PREFERRED QUALIFICATIONS

* Applicants with considerably more experience, including mid-career, are also strongly encouraged
* Strong background in statistics methodology, applications to business problems, and/or big data
* Ability to work in a fast-paced business environment
* Strong research track record
* Effective verbal and written communications skills
* Strong causal estimation based on observational data
* Strong technical communication to non-technical audience
* Experience working with very large real-world data sets and building scalable models from big data
* Expertise in SQL and a programmatic scripting language for analysis (e.g. Python/R)

The pay range for this position in Colorado is $136,000 - 184,000 per year; however, base pay offered may vary depending on job-related knowledge, skills, and experience. A sign-on bonus and restricted stock units may be provided as part of the compensation package, in addition to a full range of medical, financial, and/or other benefits, dependent on the position offered. This information is provided per the Colorado Equal Pay Act. Base pay information is based on market location. Applicants should apply via Amazon's internal or external careers site.


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.

Pursuant to the Los Angeles Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.