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Amazon Senior Applied Scientist Ads Marketing Decision Science 
United States, New York, New York 
260560902

Yesterday
DESCRIPTION

Key job responsibilities
• Define and execute a research and development plan that enables data-driven marketing decisions and delivers inspiring customer experiences
• Evaluate, evolve, and invent scientific techniques to effectively address customer needs and business problems
• Establish and drive science prototyping best practices to ensure coherence and integrity of data feeding into production ML/AI solutions
• Collaborate with colleagues across science and engineering disciplines for rapid prototyping at scale
• Partner with engineering teams to solve complex technical problems, define system-level requirements, develop implementation plans, and guide the adaptation of techniques to meet production needs
• Partner with product managers and stakeholders to define forward-looking product visions and prospective business use-cases
• Drive and lead of culture of data-driven innovation within and outside across Amazon Ads Marketing organization
• Influence organizational vision across Ads Marketing organization

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
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- Experience with popular Generative AI (Gen-AI) methods, such as supervised funetuning, reinforcement learning from human feedback (RLHF), direct preference optimization (DPO)


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.
- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
- Experience with designing, deploying and operating large scale recommender systems in production