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Applied Scientist, NA Stores Expansion

Job ID: 2668704 | Amazon.com LLC

DESCRIPTION

Amazon brings buyers and sellers together. Our retail customers depend on us to give them access to every product at the best possible price. Our sellers depend on us to give them a platform to launch their business into every home and marketplace. Making this happen is the mission of every scientist in North America Stores (NAS) organization.

To this end, the Science team is tasked with:
· Building and deploying AI / ML models that lead to exponential growth of the business.
· Organizing available data sources, and creating detailed dictionaries of data that can be used in future analyses.
· Partnering with product teams in evaluating the financial and operational impact of new product offerings.
· Partnering with science teams across other organizations to develop state of the art algorithms and models.
· Carrying out independent data-backed initiatives that can be leveraged later on in the fields of network organization, costing and financial modeling of processes.
· Publishing papers in both internal and external conferences / journals.

In order to execute the above mandate we are on the look out for smart and qualified Applied Scientists who will own projects in partnership with product and research teams as well as operate autonomously on independent initiatives that are expected to unlock benefits in the future. A past background in Artificial Intelligence is necessary, along with advanced proficiency in programming languages such as Python and C++.

Key job responsibilities
As an Applied Scientist, you are able to use a range of artificial intelligence and operations research methodologies to solve challenging business problems when the solution is unclear. You have a combination of business acumen, broad knowledge of statistics, deep understanding of ML algorithms, and an analytical mindset. You thrive in a collaborative environment, and are passionate about learning. Our team utilizes a variety of AWS tools such as Redshift, Sagemaker, Lambda, S3, and EC2 with a variety of skillsets in Tabular ML, NLP, Generative AI, Forecasting, Probabilistic ML and Causal ML. You will bring knowledge in many of these domains along with your own specialties and skill-sets.

BASIC QUALIFICATIONS

- 3+ years of building models for business application experience
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- 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 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 $222,200/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.