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Amazon Sr. Applied Scientist, Outbound Communications, Traffic and Marketing Tech in Seattle, Washington

Description

Outbound Communications own the worldwide charter for delighting our customers with timely, relevant notifications (email, mobile, SMS and other channels) to drive awareness and discovery of Amazon’s products and services. We meet customers at their channel of preference with the most relevant content at the right time and frequency. We directly create and operate marketing campaigns, and we have also enabled select partner teams to build programs by reusing and extending our infrastructure. We optimize for customers to receive the most relevant and engaging content across all of Amazon worldwide, and apply the appropriate guardrails to ensure a consistent and high-quality CX.

Outbound Communications seek a talented Sr. Applied Scientist to join our team to develop the next generation of automated and personalized marketing programs to help Amazon customers in their shopping journeys worldwide. Come join us in our mission today!

Key job responsibilities

As a Sr. Applied Scientist on the team, you will lead the roadmap and strategy for applying science to solve customer problems in the automated marketing domain. This is an opportunity to come in on Day 0 and lead the science strategy of one of the most interesting problem spaces at Amazon - understanding the Amazon customer to build deeply personalized and adaptive messaging experiences. You will be part of a multidisciplinary team and play an active role in translating business and functional requirements into concrete deliverables. You will work closely with product management and the software development team to put solutions into production.

You will apply your skills in areas such as deep learning and reinforcement learning while building scalable industrial systems. You will have a unique opportunity to produce and deliver models that help build best-in-class customer experiences and build systems that allow us to deploy these models to production with low latency and high throughput.

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

  • Experience programming in Java, C++, Python or related language

  • Experience with popular deep learning frameworks such as MxNet and Tensor Flow.

  • Experience in model evaluation

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 in reinforcement learning and natural language processing

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. This position will remain posted until filled. Applicants should apply via our internal or external career site.

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