Sr. Applied Scientist, Minerva
Amazon - Seattle, Washington, us, 98127
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Overview
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Overview
As an Applied Scientist at Amazon, you will connect with world leaders in your field working on similar problems. You will be working with large distributed systems of data and providing technical leadership to the product managers, teams, and organizations building machine learning solutions. You will be tackling Machine Learning challenges in Supervised, Unsupervised, and Semi-supervised Learning; utilizing modern methods such as deep learning and classical methods from statistical learning theory, detection, estimation. Senior Applied Scientists are specialists with the deep expertise to drive the scientific vision for our products. They are externally aware of the state-of-the-art in their respective field of expertise and are constantly focused on advancing that state-of-the-art for improving Amazon’s products and services.
Come join us as we revolutionize the book industry and deliver an amazing experience to our Kindle authors and readers.
Kindle Direct Publishing (KDP) and Print On Demand (POD) have empowered a new wave of self-motivated creators, tearing down barriers that once blocked writers from reaching readers. Our team builds rich applications and systems that empower anyone to realize their dream of becoming a published author. We strive to provide an experience that is powerful, yet simple to use and accessible to all. We focus on building tools that enable authors to design high quality digital and print books, reaching readers all around the world. This role will help ensure we maintain the trust of both our Authors and Readers by ensuring all books published to Amazon meet our standards.
Great candidates for this position will be experts in the areas of data science, machine learning, computer vision, optimization, NLP, or statistics. You will have hands-on experience with multiple science initiatives as well as be able to balance technical leadership with strong business judgment to make the right decisions about technology, models and methodological choices. You will strive for simplicity, and demonstrate significant creativity and high judgment.
Come join us as we continue to revolutionize the book industry! 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 Knowledge of programming languages such as C/C++, Python, Java or Perl Experience programming in Java, C++, Python or related language Experience with neural deep learning methods and machine learning 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. 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 . 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 . Posted:
November 21, 2024 (Updated about 16 hours ago) Amazon is an Equal Opportunity Employer – Minority / Women / Disability / Veteran / Gender Identity / Sexual Orientation / Age.
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