Applied Scientist, Ranking & Recommendation Systems, Amazon

Applied Scientist, Ranking & Recommendation Systems, Amazon

Are you passionate about AI for recommendation systems? Do you want to influence the content that customers see at Amazon.com? Our recommendation services team designs and implements scalable machine learning solutions to personalize and optimize customer experience across Amazon retail pages. We are currently expanding in New York, and are looking for an applied scientist to join us in this exciting journey.

As an Applied Scientist, you will:

1. Push the boundaries of real-world ranking, recommendation, and optimization systems.
2. Support science, engineering and product development on a scale only seen at Amazon.
3. Champion and define best practices to maximize learnings while mentoring more junior scientists and engineers.
4. Obsess over customer needs and satisfaction.
5. Create intellectual property, influence others while demonstrating significant creativity and being vocally self-critical.
6. Shape product definitions and objectives and surface signals on how these objectives meet long term customer needs.
7. Translate metrics & signals into actionable plans to calibrate individual components.
8. Operate hands-on and as an implementor of algorithms and models delivered to production systems.
9. Help define customer focused research initiatives.

Minimum Qualifications:

1. 3+ years of building models for business application experience.
2. PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience.
3. Experience programming in Java, C++, Python or related language.
4. Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing.
5. Experience using Unix/Linux.
6. Experience in professional software development.

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.