AmazonPosted 5mo ago

Senior Applied Scientist, Ad Measurements Science

Senior Applied Scientist, Ad Measurements Science at Amazon scores 96 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

US, NY, New Yorkseniorfull-time$184k-$249k

AI in this role

tensorflowscikit-learn

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 a Senior Applied Scientist on the team, you will be at the forefront of innovation, developing measurement solutions end-to-end from inception to production. You will set the technical vision and innovate on behalf of our customers. You will propose, design, analyze, and productionize models to provide novel measurement insights to our customers. You will partner with engineering to deploy these solutions into production. You will work with key stakeholders from various business teams to enable advertisers to act upon those metrics.

Key job responsibilities
* Lead the development of ad measurement models and solutions that address the full spectrum of an advertiser's investment, focusing on scalable and efficient methodologies.
* Collaborate closely with cross-functional teams including engineering, product management, and business teams to define and implement measurement solutions.
* Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models that measure the impact of ad spend across multiple platforms and timescales.
* Drive experimentation and the continuous improvement of ML models through iterative development, testing, and optimization.
* Translate complex scientific challenges into clear and impactful solutions for business stakeholders.
* Mentor and guide junior scientists, fostering a collaborative and high-performing team culture.
* Foster collaborations between scientists to move faster, with broader impact.
* Regularly engage with the broader scientific community with presentations, publications, and patents.

A day in the life
You will solve real-world problems by getting and analyzing large amounts of data, generate business insights and opportunities, design simulations and experiments, and develop statistical and ML models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the advertising organization. You will prepare written and verbal presentations to share insights to audiences of varying levels of technical sophistication.
Team video https://advertising.amazon.com/help/G4LNN5YWHP6SM9TJ

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 machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Knowledge of programming languages such as C/C++, Python, Java or Perl
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning

Preferred qualifications

- 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 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 - 183,800.00 - 248,700.00 USD annually

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Skills and AI tools this role asks for

Computer VisionNlpTensorFlowscikit-learn

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?
What are the limits of TensorFlow that you've run into, and how did you work around them?
What's a project where you used scikit-learn hands-on?
How would you decide a model or AI system is ready to ship?

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List these exact terms on your resume: Computer Vision, Nlp, TensorFlow, and scikit-learn. 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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