Enable America Jobs

Description

Calling all inventors to work on exciting new opportunities in Sponsored Products. Amazon is building a world class advertising business and defining and delivering a collection of self-service performance advertising products that drive discovery and sales of merchandise. Our products are strategically important to our Retail and Marketplace businesses, driving long-term growth. Sponsored Products (SP) helps merchants, retail vendors, and brand owners grows incremental sales of their products sold on Amazon through native advertising. SP achieves this by using a combination of machine learning, big data analytics, ultra-low latency high-volume engineering systems, and quantitative product focus. We are a highly motivated, collaborative and fun-loving group with an entrepreneurial spirit and bias for action.

You will join a newly-founded team with a broad mandate to experiment and innovate, which gives us the flexibility to explore and apply scientific techniques to novel product problems. You will have the satisfaction of seeing your work improve the experience of millions of Amazon shoppers while driving quantifiable revenue impact. More importantly, you will have the opportunity to broaden your technical skills, and be a science leader in an environment that thrives on creativity, experimentation, and product innovation.

Key job responsibilities

As a Senior Applied Scientist on this team you will:

Act as the technical leader in Machine Learning and drive full life-cycle Machine Learning projects.

Develop real-time algorithms to allocate billions of ads per day in advertising auctions.

Lead technical efforts within this team and across other teams.

Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production.

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.

Work closely with software engineers to assist in productionizing your ML models.

Research new machine learning approaches.

Recruit Applied Scientists to the team and act as a mentor to other Scientists on the team.

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

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 $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.