• Applied Scientist, SCOT Customer Instock Value

    Amazon (New York, NY)


    Description

    Are you passionate about applying causal inference and machine learning techniques to revolutionize how Amazon makes inventory decisions? Do you want to be part of a team that's reinventing how we measure the long-term impact of product availability on customer behavior throughout their entire shopping journey?

     

    The Consumer Instock Value (CIV) team within Amazon's Supply Chain Optimization Technology (SCOT) Group develops and manages systems that estimate the long-term impact of inventory availability and delivery speed changes at the product level. Our estimates are crucial inputs for multiple production systems across Amazon's supply chain planning, helping teams make critical decisions about inventory management, selection, and placement.

     

    We are seeking applied scientists to help shape our next-generation vision of modernizing how we estimate the impact of inventory decisions through innovative applications of machine learning and causal inference. We aim to create more accurate, scalable, and robust solutions that can adapt to Amazon's evolving customer shopping patterns and business needs.

    Key responsibilities include:

    - Developing innovative approaches that combine state-of-the-art AI/ML with causal inference to estimate individual treatment effects

     

    - Creating sophisticated frameworks that capture customer journey interactions and cross-product patterns using sequential data

     

    - Designing and implementing validation approaches using experiments, quasi-experimental methods, and simulations

     

    - Building scalable architectures capable of processing multiple customer interaction data streams

     

    - Collaborating with other scientists and technical teams to implement production systems that can process complex customer journey data

     

    - Leading research initiatives to resolve scientific ambiguities in applying ML methods to causal inference problems

     

    - Presenting findings to stakeholders and contributing to Amazon's research paradigms

    The ideal candidate will have:

    - Deep expertise in causal inference and machine learning

     

    - Experience with large-scale data processing and modeling

     

    - Strong research background in econometrics or related fields

     

    - Ability to bridge theoretical foundations with practical implementations

     

    - Excellence in communicating complex technical concepts to diverse audiences

     

    Your work will directly impact critical business decisions across Amazon's retail business, helping optimize the balance between inventory costs and customer experience while contributing to scientific developments in the intersection of machine learning and causal inference.

    Basic Qualifications

    - 2+ years of building models for business application experience

     

    - PhD, or Master's degree and 2+ years of CS, CE, ML or related field experience

     

    - Experience programming in Java, C++, Python or related language

     

    - Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

    Preferred Qualifications

    - Experience using Unix/Linux

     

    - Experience in professional software development

     

    Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or 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.

     

    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 $223,400/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.