Applied Scientist Ii, Strategic Account Services (sas)

Amazon Amazon · Big Tech · London, United Kingdom · Project/Program/Product Management--Technical

Applied Scientist II role focused on developing and deploying sophisticated AI solutions, including LLMs and foundation models, to improve Amazon's seller operations and internal consultancy. The role involves end-to-end development from research to production, with a focus on recommendation and optimization systems, and rigorous A/B testing.

What you'd actually do

  1. Lead the development of sophisticated AI solutions leveraging deep learning, LLMs, and advanced machine learning techniques to transform both seller operations and internal consultancy capabilities at scale
  2. Define and drive long-term scientific vision for the organization, translating complex business challenges into innovative technical solutions that advance the state-of-the-art in applied machine learning
  3. Design and implement advanced ML architectures combining multiple learning paradigms - from reinforcement learning and causal inference to predictive modeling - to tackle critical marketplace challenges
  4. Architect next-generation recommendation and optimization systems that handle complex multi-dimensional constraints while maintaining robustness and interpretability at scale
  5. Drive end-to-end development of AI applications from research through production, collaborating with engineering teams to ensure successful deployment and conducting rigorous A/B experiments to validate impact

Skills

Required

  • PhD, or a Master's degree and experience in building models for business application
  • Experience programming with at least one modern language such as Java, C++, or C# including object-oriented design
  • Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Nice to have

  • Track record of investigating, designing, and delivering innovative ML solutions that drive significant business impact

What the JD emphasized

  • autonomous drive scientific innovations
  • sophisticated AI solutions
  • LLMs
  • probabilistic modeling
  • optimization
  • prototyping and iterative improvement
  • bridging cutting models with real-world applications
  • scientific rigor
  • measurable business impact
  • deep learning
  • advanced machine learning techniques
  • long-term scientific vision
  • complex business challenges
  • state-of-the-art in applied machine learning
  • advanced ML architectures
  • reinforcement learning
  • causal inference
  • predictive modeling
  • critical marketplace challenges
  • next-generation recommendation and optimization systems
  • complex multi-dimensional constraints
  • robustness and interpretability
  • end-to-end development of AI applications
  • research through production
  • rigorous A/B experiments
  • foundation models
  • generative AI
  • sophisticated evaluation frameworks
  • Amazon's high standards for accuracy and reliability
  • technical discussions across organizational boundaries
  • complex scientific concepts
  • forefront of ML/AI research advancements
  • innovative ML solutions
  • significant business impact

Other signals

  • Develop sophisticated AI solutions
  • Translate theoretical advances into practical applications
  • Drive end-to-end development of AI applications from research through production
  • Pioneer novel applications of foundation models and generative AI