Business Intelligence Engineer, Core Shopping Data Science
AI in this role
Build and improve core shopping metrics and data pipelines leveraging LLMs to evaluate customer search experiences.
Our team leads the data science and analytics efforts for Amazon's core shopping page and we own multiple aspects of understanding how we can measure customer satisfaction with our experiences. This includes building science based insights and novel metrics to define and track customer focused aspects. We manipulate massive amounts of data, heavily leverage AI and LLMs to manipulate and make sense of terabytes worth a day, coming from multiple different systems with a high degree of complexity in structure, schema and different levels of quality.
We are looking for a Business Intelligence Engineer to build metrics, leverage LLMs, deep dive and generate insights to improve the customer experience with a high level of senior leadership visibility up the CEO. These metrics govern the quality of the search page and impact multiple organizations and teams that contribute to the search results and ultimately what customers use to make purchase decisions on the website.
Key job responsibilities
You will be building and improving metrics that measure the quality of Amazon's core shopping page leveraging LLMs. This includes working backwards from customer pain points, managing large and complex data pipelines manipulating terabytes of data a day. Your metrics and dashboards will be used by multiple teams and senior leaders and you will be providing insights into opportunities to improve the customer experience and our signals and systems. The metrics and underlying pipelines are used daily for decision making, setting up experiments, defining goals and influencing senior leaders to set priorities.
About the team
The mission of the Core Shopping Data Science team is to provide the data-driven foundation for building a world-class shopping experience that maximizes long-term free cash flow by delighting customers. We focus on the long term and big picture to ensure that the full Amazon shopping experience balances strategic trade-offs. We believe data science is the discipline of making smart decisions and we empower feature owners and systems by 1) developing and vending metrics which assess and value customer engagement, 2) building tools and datasets to inject data into the decision-making process, and 3) delivering deep analyses to inform high-touch decisions.
Basic qualifications
- 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience with data modeling, warehousing and building ETL pipelines
- Experience in Statistical Analysis packages such as R, SAS and Matlab
- Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling
- Building metrics using LLMs as a judge
- Building reusable AI based tools for measurement, deep dive and operations
Preferred qualifications
- Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift
- Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets
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 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.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, WA, Seattle - 99,500.00 - 160,000.00 USD annually
How we score this
Business Intelligence Engineer, Core Shopping Data Science at Amazon scores 60 out of 100 on AI centrality, which makes it AI Level 3 of 4 (Works on AI) on this board. The level measures how much of the work is AI, not seniority.
AI Level 3. The daily work is on or around AI systems, without necessarily building the model: remove AI and the job is hollow.
- AI Level 480 to 100
- AI Level 360 to 79
- AI Level 240 to 59
- AI Level 10 to 39
Bands come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where business intelligence was part of your work. What did you do?
- Tell me about a project where data pipeline was part of your work. What did you do?
- Tell me about a project where metrics was part of your work. What did you do?
- Tell me about a project where data analysis was part of your work. What did you do?
- Walk me through how you've used Llms in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: Business Intelligence, Data Pipeline, Metrics, Data Analysis, and Llms. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project — this role expects it to be a running habit.
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