Job Details

ID #52958189
Estado New York
Ciudad New york city
Fuente Amazon
Showed 2024-11-26
Fecha 2024-11-27
Fecha tope 2025-01-26
Categoría Etcétera
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Applied Scientist , Amazon

New York, New york city
Aplica ya

DescriptionAmazon Advertising operates at the intersection of eCommerce and advertising, and is investing heavily in building a world-class advertising business. We are defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long-term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products to improve both shopper and advertiser experience. With a broad mandate to experiment and innovate, we grow at an unprecedented rate with a seemingly endless range of new opportunities.The Ad Response Prediction team in Sponsored Products organization build advanced deep-learning models, large-scale machine-learning pipelines, and real-time serving infra to match shoppers’ intent to relevant ads on all devices, for all contexts and in all marketplaces. Through precise estimation of shoppers’ interaction with ads and their long-term value, we aim to drive optimal ads allocation and pricing, and help to deliver a relevant, engaging and delightful ads experience to Amazon shoppers. As the business and the complexity of various new initiatives we take continues to grow, we are looking for talented Applied Scientists to join the team.Key job responsibilitiesAs a Applied Scientist II, you will:

Conduct hands-on data analysis, build large-scale machine-learning models and pipelines

Work closely with software engineers on detailed requirements, technical designs and implementation of end-to-end solutions in production

Run regular A/B experiments, gather data, perform statistical analysis, and communicate the impact to senior management

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

Provide technical leadership, research new machine learning approaches to drive continued scientific innovation

Be a member of the Amazon-wide Machine Learning Community, participating in internal and external MeetUps, Hackathons and Conferences

Basic Qualifications

3+ years of building models for business application experience

PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience

Experience programming in Java, C, Python or related language

Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Preferred Qualifications

Experience using Unix/Linux

Experience in professional software development

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 $222,200/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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