Applied Scientist, Personalization & Recommendations, Machine Learning

Job summary
Want to help invent next generation technologies in recommender systems? Are you looking for roles that impact millions of customers a day, with opportunities to drive billions of dollars in impact? We’ve got the perfect job for you.

Amazon’s Personalization team is looking for an Applied Scientist to work on the core website optimization systems for all of Amazon. You will be part of a multidisciplinary team, working on one of the largest scale machine learning systems in the company. You will hone your skills in areas such as deep learning, multi-armed bandits, and reinforcement learning while building scalable industrial systems. As a member of a highly leveraged team of talented engineers and ML scientists, you will have a unique opportunity to help determine what content gets shown to every customer on Amazon.

As a member of the team, you will use machine learning and analytical techniques to create scalable solutions for business problems. You will propose and run live experiments on customers, with opportunities to publish your work. You bring strong thought leadership, great judgment, clear communication skills, and strong track record of delivery. You will play a critical role in ideation for the team. We are building the next generation optimization system that powers the biggest internet retailer on earth, and we hope you will join us!

Key job responsibilities
As an Applied Scientist, you will:
· Push the boundaries of real-world ranking, recommendation, and optimization systems
· Support science, engineering and product development on a scale only seen at Amazon.
· Champion and define best practices to maximize learnings while mentoring more junior scientists and engineers.
· Shape product definitions and objectives and surface signals on how these objectives meet long term customer needs.
· Translate metrics & signals into actionable plans to calibrate individual components.
· Operate hands-on and as an implementer of algorithms and models delivered to production systems.
· Conduct rigorous evaluation of models through offline validation and experiments run on live systems.
· Foster a collaborative environment by active participation in model and experiment reviews, journal clubs and other science meetings.
· Help define customer focused research initiatives.

A day in the life
The mission of Amazon’s content optimization system is to enable innovation on behalf of internal content providers by making it easy to get the right content in front of customers at the right time. An applied scientist will define product objectives, define relevant signals and use those to train, validate, and deploy models. We operate in a collaborative environment where you will be expected to provide and solicit feedback and help spread knowledge and learnings.

About the team
Amazon’s Content Optimization System team is responsible for tailoring the experience of every customer on Amazon’s most prominent pages, billions of times a day. Utilizing state of the art machine learning techniques, we build highly scalable, real-time systems that determine the content customers see across the Amazon website.

Basic Qualifications

· PhD or equivalent Master's Degree plus 4+ years of experience in CS, CE, ML or related field
· 2+ years of experience of building machine learning models for business application
· Experience programming in Java, C++, Python or related language

Preferred Qualifications

· PhD in Computer Science, ML, Statistics or related field
· 5+ years of industry experience
· Extensive experience building predictive and optimization models
· Expert knowledge and practical experience in several of the following areas: machine learning, statistics, deep learning, natural language processing, informational retrieval
· Proficiency in at least one modern programming language such as Java, C, C++, C#, Python
· Excellent problem solving skills
· Excellent written and oral communication skills - Significant peer reviewed scientific contributions in premier journals and conferences
· Proven track record leading, mentoring, and growing teams of scientists
· Highly motivated self-starter with bias for innovative thinking
· Experience in advertising, website optimization, recommender systems



Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.