Description

　　Join us in the evolution of Amazon’s Seller business! The Selling Partner Recruitment and Success organization is the growth and development engine for our Store. Partnering with business, product, and engineering, we catalyze SP growth with comprehensive and accurate data, unique insights, and actionable recommendations and collaborate with WW SP facing teams to drive adoption and create feedback loops. We strongly believe that any motivated SP should be able to grow their businesses and reach their full potential supported by Amazon tools and resources.

　　We are looking for a Senior Applied Scientist to lead us to identify data-driven insight and opportunities to improve our SP recruitment strategy and drive new seller success. As a successful applied scientist on our talented team of scientists and engineers, you will solve complex problems to identify actionable opportunities, and collaborate with engineering, research, and business teams for future innovation. You need to be a sophisticated user and builder of statistical models and put them in production to answer specific business questions. We prefer candidates with strong causal ML knowledge. You are an expert at synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication. You will continue to contribute to the research community, by working with scientists across Amazon, as well as collaborating with academic researchers and publishing papers (www.aboutamazon.com/research).

　　Key job responsibilities

　　As a Sr. Applied Scientist in the team, you will:

　　Identify opportunities to improve SP recruitment and development process and translate those opportunities into science problems via principled statistical solutions (e.g. ML, causal, RL).

　　Mentor and guide the applied scientists in our organization and hold us to a high standard of technical rigor and excellence in MLOps.

　　Design and lead roadmaps for complex science projects to help SP have a delightful selling experience while creating long term value for our shoppers.

　　Work with our engineering partners and draw upon your experience to meet latency and other system constraints.

　　Identify untapped, high-risk technical and scientific directions, and simulate new research directions that you will drive to completion and deliver.

　　Be responsible for communicating our science innovations to the broader internal & external scientific community.

　　We are open to hiring candidates to work out of one of the following locations:

　　New York, NY, USA | Seattle, WA, USA

　　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.

　　Experience with causal inference and causal ML models

　　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 $136,000/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. Applicants should apply via our internal or external career site.