Sr. Applied Scientist, Brand Intellegence and Agentic AI Program
Sr. Applied Scientist, Brand Intellegence and Agentic AI Program
Posted on :
06-08-2025
Employer Active
1 Vacancy
Job Alert
You will be updated with latest job alerts via emailValid email field required
Send jobs
Share
Send me jobs like this
Job Alert
You will be updated with latest job alerts via emailValid email field required
Send jobs
Job Description
The Brand Intelligence and Agentic AI program represents the voice of customers with respect to brands: brand recognition reputation value quality and overall appeal. The team as part of Amazon Retail tech org is hiring an experienced Sr. Applied Scientist to guide the team on science strategies design ML solutions (including the new Agentic AI agents) to solve customer-facing brand shopping challenges at scale and grow and influence the broader science community. The team uses Machine Learning Deep Learning LLM and Agentic AI to derive actionable insights from understanding customer shopping intent and preferences on brands (well recognized brands premium brands new and trending brands relevant top brands matching customer searches or individual customer-level brand preference) and develop and experiment with ML solutions to deliver business impact. We also start working with marketing team to build AI agents to assist them better in their marketing journey with brands insights and knowledge.
We are a science-focused team incubating and building disruptive solutions to solve large-scale shopping recommendation and personalization problems to assist our customers easily discover relevant and valuable brands and selection as well as to help brand owners being successful to reach promising customers.
This is a unique high visibility opportunity for someone who wants to have direct impact solving customer-facing problems dive deep into large-scale science problems the new Agentic AI world enable measurable actions on the Consumer economy and work closely with scientists.
Key job responsibilities
Work with Product and Engineering to shape the product vision and provide forward-looking science strategies.
Lead scientists design and build machine-learning and LLM solutions.
Collaborate with partner teams on customer-facing search and browse experiences that will utilize the data and ML models to better serve customers brand shopping experience.
Perform hands-on data analysis build machine-learning models run regular A/B tests and communicate the impact to senior management.
Drive continued scientific innovations (Agentic AI e.g.) as a thought leader and practitioner and understand marketing needs on brands understanding.
Provide technical and career development guidance to both scientists and engineers in the organization.
- 3 years of building machine learning models for business application experience
- PhD or Masters degree and 3 years of applied research experience
- Experience programming in Java C Python or related language
- Experience with neural deep learning methods and machine learning
- Experience with modeling tools such as R scikit-learn Spark MLLib MxNet Tensorflow numpy scipy etc.
- Experience with large scale distributed systems such as Hadoop Spark etc.
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 for more information. If the country/region youre applying in isnt 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 $150400/year in our lowest geographic market up to $260000/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 This position will remain posted until filled. Applicants should apply via our internal or external career site.
We are a science-focused team incubating and building disruptive solutions to solve large-scale shopping recommendation and personalization problems to assist our customers easily discover relevant and valuable brands and selection as well as to help brand owners being successful to reach promising customers.
This is a unique high visibility opportunity for someone who wants to have direct impact solving customer-facing problems dive deep into large-scale science problems the new Agentic AI world enable measurable actions on the Consumer economy and work closely with scientists.
Key job responsibilities
Work with Product and Engineering to shape the product vision and provide forward-looking science strategies.
Lead scientists design and build machine-learning and LLM solutions.
Collaborate with partner teams on customer-facing search and browse experiences that will utilize the data and ML models to better serve customers brand shopping experience.
Perform hands-on data analysis build machine-learning models run regular A/B tests and communicate the impact to senior management.
Drive continued scientific innovations (Agentic AI e.g.) as a thought leader and practitioner and understand marketing needs on brands understanding.
Provide technical and career development guidance to both scientists and engineers in the organization.
- 3 years of building machine learning models for business application experience
- PhD or Masters degree and 3 years of applied research experience
- Experience programming in Java C Python or related language
- Experience with neural deep learning methods and machine learning
- Experience with modeling tools such as R scikit-learn Spark MLLib MxNet Tensorflow numpy scipy etc.
- Experience with large scale distributed systems such as Hadoop Spark etc.
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 for more information. If the country/region youre applying in isnt 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 $150400/year in our lowest geographic market up to $260000/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 This position will remain posted until filled. Applicants should apply via our internal or external career site.
Required Experience:
Senior IC
Employment Type
Full-Time
Key Skills
Report This Job
Disclaimer: Drjobpro.com is only a platform that connects job seekers and employers. Applicants are advised to conduct their own independent research into the credentials of the prospective employer.We always make certain that our clients do not endorse any request for money payments, thus we advise against sharing any personal or bank-related information with any third party. If you suspect fraud or malpractice, please contact us via contact us page.
