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Description

The Brand Experience (BX) organization creates, builds and leads innovation for Brand Owners in the Amazon store worldwide. Our growing portfolio of BX tools enable Brands to tell their unique Brand and product stories, optimize content through statistical experimentation, solicit and manage reviews to earn Customer trust and grow their business as well as engage with new and loyal Customers through a variety of channels.

We are looking for a Senior Applied Scientist to lead evaluation and continuous improvement of our text and image generation models to enhance Brands’ success on Amazon. You will collaborate with product and engineering teams to identify gaps in existing tools, develop requirements for new models, and guide overall platform enhancements. You will also drive research initiatives to innovate on generation capabilities. Your ideas will guide engineering priorities and process refinements.The ideal candidate will have a strong intuition for refining model prompts and outputs to improve quality and expand use cases. The role also includes aggregating learnings across science teams, uncovering insights, and continually building upon our knowledge to inform future roadmaps. We support and encourage publishing papers to contribute to the broader research community.

As Applied Scientist on this team, you will:

• Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale and complexity.

• Build Machine Learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models.

• Run A/B experiments, gather data, and perform statistical analysis.

• Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving.

• Identify and action data collection and labeling in conjunction with team members.

• Research new and innovative machine learning approaches.

• Present results and explain methods to senior leadership.

• Willingness to publish research at internal and external top scientific venues.

• Write and pursue IP submissions.

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.

• Effective verbal and written communication skills with non-technical and technical audiences.

• Experience working with large real-world data sets and building scalable models from big data.

• Exhibits excellent business judgment; balances business, product, and technology very well.

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