Description 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. Basic Qualifications - 4+ years of applied research experience - 2+ years of building machine learning models for business application experience - PhD, or Master's degree and 2+ years of applied research experience - Experience programming in Java, C++, Python or related language Preferred Qualifications - Experience in building machine learning models for business application 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. 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 $150,400/year in our lowest geographic market up to $260,000/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.