Sr. Applied Scientist - GenAI Impact Modeling , Catalog Experimentation and Impact Measurement
Amazon.com
This position gives you an opportunity to build metrics that shape Amazon's catalog initiatives world wide. If that rings a bell and if you possess the confidence to navigate through early stage ambiguities, read on.
Amazon Selection and Catalog Systems (ASCS) builds the systems that host and run the world’s largest e-Commerce products catalog - it powers the online buying experience for customers worldwide so they can find, discover and buy anything they want. Amazon’s customers rely on the completeness, consistency and correctness of Amazon's product data to make well-informed purchase decisions. Improving the quality of product data is a continuous process. It requires data driven decisions on what product data changes simplify and improve the Customers’ experience.
Our team seeks a Sr. Applied Scientist with demonstrated experience in experimentation techniques and causal inference at scale. Our problems include attributing values to actions in complex world of catalog information driving customer behavior. The ideal candidate combines acumen in data science and causal modeling to grapple with these and other challenges and guide decision-making at the highest levels. This is an opportunity to influence catalog quality improvements across Amazon.
Key job responsibilities
1. Build models to attribute customer impact to specific LLM generated product data quality improvements. You will need high judgment for balancing cost efficiency of your models with accuracy of the estimates.
2. Partner with Product Managers and Engineering to build and scale new customer experience metrics
3. Build new business metrics in the A/B experimentation platform
4. Guide quality improvement programs by generating actionable insights
About the team
We enable teams across Amazon to run A/B experiments on product listings through Catalog Experimentation Program. Additionally, using experimentation and causal inference models, we build customer impact metrics for different experiences in Amazon stores world wide. We help Catalog data quality initiatives understand the customer impact of their work streams and influence their priorities to maximize customer benefits.
Amazon Selection and Catalog Systems (ASCS) builds the systems that host and run the world’s largest e-Commerce products catalog - it powers the online buying experience for customers worldwide so they can find, discover and buy anything they want. Amazon’s customers rely on the completeness, consistency and correctness of Amazon's product data to make well-informed purchase decisions. Improving the quality of product data is a continuous process. It requires data driven decisions on what product data changes simplify and improve the Customers’ experience.
Our team seeks a Sr. Applied Scientist with demonstrated experience in experimentation techniques and causal inference at scale. Our problems include attributing values to actions in complex world of catalog information driving customer behavior. The ideal candidate combines acumen in data science and causal modeling to grapple with these and other challenges and guide decision-making at the highest levels. This is an opportunity to influence catalog quality improvements across Amazon.
Key job responsibilities
1. Build models to attribute customer impact to specific LLM generated product data quality improvements. You will need high judgment for balancing cost efficiency of your models with accuracy of the estimates.
2. Partner with Product Managers and Engineering to build and scale new customer experience metrics
3. Build new business metrics in the A/B experimentation platform
4. Guide quality improvement programs by generating actionable insights
About the team
We enable teams across Amazon to run A/B experiments on product listings through Catalog Experimentation Program. Additionally, using experimentation and causal inference models, we build customer impact metrics for different experiences in Amazon stores world wide. We help Catalog data quality initiatives understand the customer impact of their work streams and influence their priorities to maximize customer benefits.
Confirm your E-mail: Send Email
All Jobs from Amazon.com