Amazon Worldwide Advertising is one of Amazon's fastest growing and most profitable businesses.Partner Enablement and Growth (PEG) team leverages advanced analytics and experimentation to unlock value for our advertising partners and mutual customers i.e. the advertisers. The Science charter in PEG spearheads these efforts by optimizing the onboarding journey and providing data-driven insights to agencies and tool providers in the Amazon Ads ecosystem. Our proprietary tools provide granular visibility into what's working across audiences, creatives, targeting strategies, and more. These custom algorithmic recommendations uncover growth opportunities previously hidden in the data fog. Partners can confidently allocate budgets based on predicted performance, unlocking improved return on ad spend. And advertisers reap the benefits through expanded brand awareness, increased sales, and stronger customer loyalty over time.
Key job responsibilities
You will participate in driving features from idea to deployment, and your work will directly impact millions of customers.
You are going to love this job because you will:
- Map business requirements and customer needs to a scientific problem.
- Align the research direction to business requirements and make the right judgments on research/development schedule and prioritization.
- Research, design and implement scalable machine learning (ML), natural language, or computational models to solve problems that matter to our customers in an iterative fashion.
- Mentor and develop junior applied scientists and developers who work on applied science problems in the same organization.
- Stay informed on the latest machine learning, natural language and/or artificial intelligence trends and make presentations to the larger engineering and applied science communities.
About the team
The Partner Science team’s vision is to drive the Advertising Partner flywheel by infusing science-based interventions at all stages of their journey with Amazon Ads including demand generation, partner selection, partner engagement, partner competition and innovation, ultimately improving the partner managed advertiser experience. We drive the vision by attracting and growing top scientist talents in an innovative, experimental, and collaborative culture.- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- Experience with causal inference for 1+ year and prototyping experience using LLM/Gen AI models- 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.
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.
