Applied Scientist, Ads Measurement Science

Amazon is hiring an Applied Scientist, Ads Measurement Science in New York, United States. It pays $172k-$223k a year and Level rates it ; you can apply on Amazon.

Level's read on this job

On Level's scale, this applied scientist role is Builds AI ●●●●: the job is building AI systems.
The listed pay starts close to the typical applied scientist posting on Level.
Tools and skills the posting cites include computer vision and NLP.
Level lists 20,000+ other open roles at Amazon.
The role was posted in the last few days.

AI in this role

computer-visionnlp

The Ads Measurement Science team in the Measurement, Ad Tech, and Data Science (MADS) team of Amazon Ads serves a centralized role developing solutions for a multitude of performance measurement products. We create solutions which measure the comprehensive impact of advertiser's ad spend, including sales impacts both online and offline and across timescales, and provide actionable insights that enable our advertisers to optimize their media portfolios. We also own the science solutions for AI tools that unlock new insights and automate high-effort customer workflows, such as custom query and report generation based on natural language user requests. We leverage a host of scientific technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision.

As an Applied Scientist on the team, you will lead measurement solutions end-to-end from inception to production. You will propose, design, analyze, and productionize models to provide novel measurement insights to our customers.

Key job responsibilities
Leverage deep expertise in one or more scientific disciplines to invent solutions to ambiguous ads measurement problems
Disambiguate problems to propose clear evaluation frameworks and success criteria
Work autonomously and write high quality technical documents
Implement a significant portion of critical-path code, and partner with engineers to directly carry solutions into production
Partner closely with other scientists to deliver large, multi-faceted technical projects
Share and publish works with the broader scientific community through meetings and conferences
Communicate clearly to both technical and non-technical audiences
Contribute new ideas that shape the direction of the team's work
Mentor more junior scientists and participate in the hiring process

About the team
We are a team of scientists across Applied, Research, Data Science and Economist disciplines. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and Causal Inference with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, sales leaders, and other scientists with expertise in the ads industry and on building scalable modeling and software solutions.

Basic qualifications

- 3+ years of building models for business application experience
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Preferred qualifications

- Experience using Unix/Linux
- Experience in professional software development

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, NY, New York - 172,400.00 - 223,400.00 USD annually

How we rate this

Applied Scientist, Ads Measurement Science at Amazon rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Applied Scientist, Ads Measurement Science is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $172k-$223k a year.
The role is based in US, NY, New York.
The job was posted on November 9, 2026.
Seniority: mid.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Computer visionNLP

Also common in applied scientist jobs at this level

Machine learning Deep learning LLM Reinforcement learning TensorFlow Spark

Pay

This job starts at: $172k
Typical applied scientist job: $169k

This posting starts close to the typical applied scientist job. See applied scientist jobs

Questions you could be asked

Walk me through a computer vision problem you solved, from raw data to a deployed model.
What NLP problem have you worked on, and how did you measure whether it actually worked?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: Computer vision and NLP. 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.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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