Sr. Applied Scientist, Amazon Private Brands
Job Description
Join our Amazon Private Brands Selection Guidance organization in building science and tech solutions at scale to delight our customers with products across our leading private brands such as Amazon Basics, Amazon Essentials, and by Amazon.The Selection Guidance team applies Generative AI, Machine Learning, Statistics, and Economics solutions to drive our private brands product assortment, strategic business decisions, and product inputs such as title, price, merchandising and ordering. We are an interdisciplinary team of Scientists, Economists, Engineers, and Product Managers incubating and building day one solutions using novel technology, to solve some of the toughest business problems at Amazon.As a Sr. Applied Scientist you will invent novel solutions and prototypes, and directly contribute to bringing your ideas to life through production implementation. Current research areas include entity resolution, agentic AI, large language models, and product substitutes. You will review and guide scientists across the team on their designs and implementations, and raise the team bar for science research and prototypes.This is a unique, high visibility opportunity for someone who wants to develop ambitious science solutions and have direct business and customer impact.Key job responsibilities- Partner with business stakeholders to deeply understand APB business problems and frame ambiguous business problems as science problems and solutions.- Invent novel science solutions, develop prototypes, and deploy production software to solve business problems.- Review and guide science solutions across the team.- Publish and socialize your and the team's research across Amazon and external avenues as appropriate- Leverage industry best practices to establish repeatable applied science practices, principles & processes.
Locations
- Canada, BC, Vancouver, Vancouver, BC, Canada
Salary
Salary not disclosed
Estimated Salary Rangemedium confidence
180,000 - 260,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- - 3+ years of building machine learning models for business application experienceintermediate
- - PhD, or Master's degree and 6+ years of applied research experienceintermediate
- - Experience programming in Java, C++, Python or related languageintermediate
- - Experience with neural deep learning methods and machine learningintermediate
Required Qualifications
- - 3+ years of building machine learning models for business application experience (experience, 3 years)
- - PhD, or Master's degree and 6+ years of applied research experience (experience, 6 years)
- - Experience programming in Java, C++, Python or related language (experience)
- - Experience with neural deep learning methods and machine learning (experience)
Preferred Qualifications
- - Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc. (experience)
- - Experience with large scale distributed systems such as Hadoop, Spark etc. (experience)
- - Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability (experience)
- The base salary for this position ranges from $195,900/year up to $327,200/year. Salary is based on a number of factors 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. Applicants should apply via our internal or external career site. (experience)
Responsibilities
- - Partner with business stakeholders to deeply understand APB business problems and frame ambiguous business problems as science problems and solutions.
- - Invent novel science solutions, develop prototypes, and deploy production software to solve business problems.
- - Review and guide science solutions across the team.
- - Publish and socialize your and the team's research across Amazon and external avenues as appropriate
- - Leverage industry best practices to establish repeatable applied science practices, principles & processes.
