Senior Applied Scientist, Brand Shopping Experiences

Amazon; Seattle (WA); On-site; USD 150,000 - 260,000; Full time

Job summary

A leading company in e-commerce is looking for a Senior Applied Scientist to enhance brand visibility through machine learning. You will lead projects, create insights from data, and develop algorithms that empower brands to connect with consumers effectively. Join a dynamic team committed to shaping the future of shopping experiences, leveraging advanced analytics to drive growth.

Benefits

Competitive compensation package

Career growth opportunities

Inclusive company culture

Qualifications

• 3+ years of building machine learning models for business applications.
• Knowledge of programming languages such as C/C++, Python, Java.
• Experience with neural networks and machine learning methods.

Responsibilities

• Lead machine learning projects across teams.
• Perform hands-on analysis and modeling.
• Build and optimize machine learning models for production.

Skills

Machine Learning

Data Analysis

Statistical Analysis

Education

PhD or Master's degree in relevant field

Tools

Python

Java

C++

Job description

Senior Applied Scientist, Brand Shopping Experiences

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Senior Applied Scientist, Brand Shopping Experiences

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Description

Brand Stores (such as www.amazon.com/lego) are a core product offering in the Amazon Advertising portfolio. The brand’s store is their dedicated place on Amazon to differentiate, grow sales, and build loyalty with millions of shoppers. Our mission is to empower brands of all sizes to tell their story in their own unique voice to consumers. We help brands create engaging shopping experiences that assist shoppers in discovering and evaluating them as part of purchase decisions. We succeed when we are both useful to shoppers and when brands can attract and retain shopper’s attention using our products. A cool case study on brand stores can be found here: https://advertising.amazon.com/library/case-studies/nespresso-brand-store-increases-shopper-engagement.

Description

Brand Stores (such as www.amazon.com/lego) are a core product offering in the Amazon Advertising portfolio. The brand’s store is their dedicated place on Amazon to differentiate, grow sales, and build loyalty with millions of shoppers. Our mission is to empower brands of all sizes to tell their story in their own unique voice to consumers. We help brands create engaging shopping experiences that assist shoppers in discovering and evaluating them as part of purchase decisions. We succeed when we are both useful to shoppers and when brands can attract and retain shopper’s attention using our products. A cool case study on brand stores can be found here: https://advertising.amazon.com/library/case-studies/nespresso-brand-store-increases-shopper-engagement.

We are looking for a Senior Applied Scientist to lead the generation of data driven insights that bring long term value to brands, as well as the ideation and creation of ranking models for brand content. In this role you will influence our team’s science and business strategy with your analyses. You will be expected to identify and solve ambiguous problems and science deficiencies, and to provide informed solutions based on state of the art machine learning research.

Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team collaborate closely with other advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate.

Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding.

Team video https://youtu.be/zD_6Lzw8raE

#adpt-brand-shopping-experiences-science

Key job responsibilities

As a Senior Applied Scientist on this team, you will:

• Be the technical leader in Machine Learning; lead efforts within this team and across other teams.
• Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience.
• Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, 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.
• Research new and innovative machine learning approaches.
• Recruit Applied Scientists to the team and provide mentorship.
  
  

About The Team

The Brand Shopping Experience Science and Analytics Team develops and deploys into production Machine Learning Algorithms that quantify relevance, select and organize Brands’ pieces of content in different placements in Amazon.com. Our goal is to create engaging and enjoyable shopping experiences that incentivize Brand discovery and that foster Brand-Customer relationships.

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
• Knowledge of programming languages such as C/C++, Python, Java or Perl
• Experience programming in Java, C++, Python or related language
• Experience with neural deep learning methods and machine learning
  
  

Preferred Qualifications

• Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability
• Experience with large scale distributed systems such as Hadoop, Spark etc.
  
  

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

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.


Company - Amazon.com Services LLC

Job ID: A2946427

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Research, Science, and Engineering

• Industries

  Software Development

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