Senior Applied Scientist, Product Knowledge

Company:  Amazon
Location: Seattle
Closing Date: 08/11/2024
Salary: £125 - £150 Per Annum
Hours: Full Time
Type: Permanent
Job Requirements / Description

Senior Applied Scientist, Product Knowledge

Job ID: 2773266 | Amazon.com Services LLC

Lead the development of cutting-edge AI models to power Amazon's eCommerce ontology - the authoritative source of product knowledge driving exceptional customer experiences.

Applied Scientists in this role solve problems related to product classification, attribute extraction, ontology modeling, data integration and enrichment, and scalable knowledge services. It's challenging due to the vast scale, heterogeneous data sources, and evolving domains, but exciting for pushing boundaries in ML, NLP, and knowledge representation research.

If you're passionate about driving innovation at scale, we want to hear from you!

Key job responsibilities

  1. Lead the research and development of novel AI solutions to enrich and curate Amazon's product ontology (Product Knowledge) at scale
  2. Develop scalable data processing pipelines and architectures to ingest, transform, and enrich product data from various sources (seller listings, customer reviews, etc.)
  3. Collaborate with engineers to design and implement robust services
  4. Work closely with product managers, stakeholders, and subject matter experts to identify opportunities for innovation and drive the roadmap for Product Knowledge
  5. Mentor and upskill junior scientists and engineers, fostering a culture of continuous learning and knowledge sharing
  6. Communicate complex technical concepts and research findings effectively to diverse audiences, including leadership, cross-functional teams, and the wider scientific community
  7. Stay up-to-date with the latest advancements in machine learning, natural language processing, knowledge representation, and related fields, and identify opportunities to apply them to Product Knowledge

A day in the life

The Amazon product ontology is a structured knowledge base representing product types, attributes, classes, and relationships. It standardizes product data, enabling enhanced customer experiences through improved search and recommendations, streamlined selling processes, and internal data enrichment across Amazon's eCommerce ecosystem.

You will work with following stakeholders:

  • Product Managers represent customer experiences and selling partner experiences
  • Category Leaders (e.g., apparel, electronics) provide domain knowledge and guidance as subject matter experts
  • Engineers build and maintain data pipelines and services in production
  • Ontologists design data models and define guidelines
  • Other Applied Scientists collaborate on research and innovation

About the team

The Product Knowledge team at Amazon is dedicated to creating the industry-standard eCommerce product and services ontology. Our diverse team of applied scientists, engineers, ontologists and subject matter experts build a comprehensive ontology enabling exceptional customer and selling partner experiences through high-quality, contextual product knowledge at scale.

BASIC QUALIFICATIONS

  • 4+ years of applied research experience
  • 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.

Posted: August 23, 2024 (Updated about 3 hours ago)

#J-18808-Ljbffr
Apply Now
An error has occurred. This application may no longer respond until reloaded. Reload 🗙