Senior Applied Scientist, Special Projects

Amazon Amazon · Big Tech · Seattle, WA · Applied Science

Senior Applied Scientist role focused on building state-of-the-art ML models for healthcare challenges. Responsibilities include designing and implementing novel AI/ML solutions, driving advancements in ML and data science, balancing theoretical knowledge with practical implementation, and collaborating with cross-functional teams. The role emphasizes early-stage product development and establishing best practices for ML experimentation, evaluation, development, and deployment.

What you'd actually do

  1. Design and implement novel AI/ML solutions for complex healthcare challenges
  2. Drive advancements in machine learning and data science
  3. Balance theoretical knowledge with practical implementation
  4. Work closely with customers and partners to understand their requirements
  5. Navigate ambiguity and create clarity in early-stage product development

Skills

Required

  • 5+ 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 deep learning models for healthcare
  • Experience designing novel NLP/ML architectures, including pretraining, fine-tuning, and post-training techniques.

What the JD emphasized

  • building machine learning models for business application
  • pretraining, fine-tuning, and post-training techniques

Other signals

  • state-of-the-art ML models
  • healthcare challenges
  • advancements in machine learning and data science
  • practical implementation
  • early-stage product development
  • ML experimentation, evaluation, development and deployment
  • define roadmap and strategic initiatives
  • building machine learning models for business application
  • neural deep learning methods
  • pretraining, fine-tuning, and post-training techniques