Principal Applied Scientist, Personalization

Key job responsibilities

• Innovate new features and models that have huge impact on the customer experience. Help customers find the right products and content on their shopping journey.
• Leverage the use of advanced machine learning to create customer shopping experience at Amazon's scale - for all Amazon customers across all countries in realtime
• Be a key leader on a multidisciplinary team across science, product, design, and engineering to see through ideas from inception, prototype, to launch in the hands of all Amazon's customers
• Drive the science roadmap across multiple teams, helping coordinate a cohesive science agenda across the org.
• Mentoring applied scientists across the org, growing their skills and careers.

About the team

Our mission is to delight every Amazon customer with a personalized shopping experience tailed to their intent. We achieve our mission through investments in Science, UX, and central systems with the purpose of delivering the future of shopping on Amazon. We are seeking a Principal Applied Scientist to lead the science charter across the recommendations and intent identification space.

Basic Qualifications

• PhD in Computer Science, Machine Learning, Statistics, or related field, OR Master's degree and 6+ years of applied research experience
• 5+ years of building machine learning models for business applications, with proven track record of shipping ML-powered products to production
• Deep expertise in machine learning engineering with hands-on experience building and deploying models at scale
• Strong programming skills in Python, Java, C++, or related languages with ability to write production-quality code
• Experience mentoring junior scientists and engineers
• Experience distilling informal customer requirements into problem definitions, dealing with ambiguity and competing objectives

Preferred Qualifications

• Experience creating novel algorithms and advancing the state of the art
• Experience communicating with users, other technical teams, and management to collect requirements, describe software product features, and technical designs
• Publishations at top-tier peer-reviewed conferences (NeurIPS, ICML, ICLR, CVPR, ICCV, KDD, RecSys) or patents demonstrating technical innovation
• Track record of successful production ML deployments at scale with measurable business impact
• Strategic thinking combined with strong execution capability and bias for action
• Experience bridging research with practical engineering implementation
• Technical leadership experience in fast-paced, ambiguous environments