Sr. Applied Scientist, Pxt Central Science

Amazon Amazon · Big Tech · San Francisco, CA · Applied Science

Senior Applied Scientist role focused on building and deploying production machine learning systems for business applications, including personalization, NLP, and generative AI, impacting millions of users and driving strategic decisions. The role involves designing, developing, and deploying ML solutions, partnering on causal inference models, and collaborating with cross-functional teams.

What you'd actually do

  1. Design and deploy large-scale machine learning systems in production environments
  2. Develop innovative ML solutions using state-of-the-art techniques including deep learning, NLP, and generative AI
  3. Create ML solutions that personalize manager onboarding and development experiences — identifying individual capability gaps, recommending tailored learning pathways, and measuring development effectiveness across diverse manager populations and contexts
  4. Partner to build causal inference models and experimental frameworks to measure impact
  5. Collaborate with product managers, engineers, and business leaders to define technical roadmaps

Skills

Required

  • 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

Nice to have

  • 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.
  • Experience in driving end to end delivery, and communicating results to senior leadership
  • Experience with leading experienced scientists as well as having a record of developing junior members from academia or industry to a career track in a business environment
  • Experience with building LLMs, generative AI applications on AWS
  • Experience with personalization, recommendation systems, or adaptive learning systems

What the JD emphasized

  • building machine learning models for business application experience
  • deep learning
  • generative AI applications

Other signals

  • build production machine learning systems
  • solve complex business problems at scale
  • directly impact millions of users
  • drive strategic decision-making
  • personalize manager onboarding and development experiences
  • identify individual capability gaps
  • recommending tailored learning pathways
  • measuring development effectiveness
  • build causal inference models
  • experimental frameworks to measure impact
  • deep learning
  • NLP
  • generative AI applications