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Description

Amazon is looking for a Senior Applied Scientist to help build next generation selection/assortment systems. On the Core Selection team within the Supply Chain Optimization Technologies (SCOT) organization, we own the selection to determine which products Amazon offers globally. We build tools and systems that enable our partners and business owners to scale themselves by leveraging our problem domain expertise, focusing instead on introspecting our outputs and iteratively helping us improve our ML models rather than hand-managing their assortment. We partner closely with our business stakeholders as we work to develop state-of-the-art, scalable, automated selection management systems.

As a Senior Applied Scientist, you will work with software engineers, product managers, and business teams to understand the business problems and requirements, distill that understanding to crisply define the problem, and design and develop innovative solutions to address them. Our team is highly cross-functional and employs a wide array of scientific tools and techniques to solve key challenges, including supervised and unsupervised machine learning, non-convex optimization, causal inference, natural language processing, linear programming, reinforcement learning, and other forecast algorithms. Some critical research areas in our space include modeling substitutability between similar products, incorporating basket awareness and complementarity-aware logic, measuring speed sensitivity of products, modeling network capacity constraints, and supply and demand forecasting.

Key job responsibilities

You will be a science tech leader for the team. As a Senior Applied Scientist you will:

• Lead a team of scientists to innovate on state-of-the-art assortment planning systems for limited-shelf businesses.

• Set the scientific strategic vision for the team. You lead the decomposition of problems and development of roadmaps to execute on it.

• Set an example for other scientists with exemplary scientific analyses; maintainable, extensible, and well-tested code; and simple, intuitive, and effective solutions.

• Influence team business and engineering strategies.

• Exercise sound judgment to prioritize between short-term vs. long-term and business vs. technology needs.

• Communicate clearly and effectively with stakeholders to drive alignment and build consensus on key initiatives.

• Foster collaborations between scientists across Amazon researching similar or related problems.

• Actively engage in the development of others, both within and outside the team.

• Engage with the broader scientific community through presentations, publications, and patents.

To help describe some of our challenges, we created a short video about SCOT at Amazon: http://bit.ly/amazon-scot

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

Austin, TX, USA | Bellevue, WA, USA | New York, NY, 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

• 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 $147,100/year in our lowest geographic market up to $286,000/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. This position will remain posted until filled. Applicants should apply via our internal or external career site.