Applied Scientist, Amazon Ads, Forecasting, Insights and Guidance (FIG)

Description

• Help build Amazon's world-class advertising business by applying machine learning and generative AI to hard problems in campaign optimization. As an Applied Scientist, you'll take models from problem formulation

Description

• Help build Amazon's world-class advertising business by applying machine learning and generative AI to hard problems in campaign optimization. As an Applied Scientist, you'll take models from problem formulation to production, working closely with engineering and product partners to deliver measurable advertiser impact. - Experience training or adapting large language models - Experience with sequential decision-making or optimization under uncertainty - Experience with causal inference or rigorous evaluation of ML systems - Experience designing experiments and performing statistical analysis of results - Experience with modern deep learning frameworks such as PyTorch Key job responsibilities - Lead end-to-end ML initiatives with high ambiguity, scale, and complexity, from problem framing through production deployment - Develop models that improve advertiser outcomes on Amazon's advertising platform - Design and execute experiments and statistical analyses to validate real-world impact - Partner with engineering and product to ship science into customer-facing products responsibly - Advance the state of the art by researching new techniques and applying them to advertising problems About the team You'll join a highly motivated, collaborative, and entrepreneurial team with a broad mandate to experiment, innovate, and break new ground. Your work will directly influence advertiser success and shape the future of Amazon Advertising.

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