Applied Scientist II, Strategic Account Services (SAS) in London

Applied Scientist II, Strategic Account Services (SAS) in London

London Full-Time 60000 - 80000 £ / year (est.) No working from home possible
Amazon Science

At a Glance

  • Tasks: Drive scientific innovations and develop AI solutions for Amazon's global seller base.
  • Company: Join Amazon's Strategic Account Services team, a leader in e-commerce consultancy.
  • Benefits: Competitive salary, diverse work culture, and opportunities for professional growth.
  • Other info: Be part of a diverse team that values innovation and inclusivity.
  • Why this job: Make a real impact with cutting-edge AI technology in a collaborative environment.
  • Qualifications: PhD or Master's in relevant field, programming skills, and experience in machine learning.

The predicted salary is between 60000 - 80000 £ per year.

Amazon Strategic Account Services (SAS) Tech Organization is looking for an Applied Scientist who can autonomously drive scientific innovations from research to production, developing sophisticated AI solutions that serve both Amazon’s global seller base and internal Marketplace Consultants. Working in a highly collaborative environment, you’ll leverage expertise in machine learning, operations research, and statistics to translate theoretical advances in large language models, probabilistic modeling, and optimization into practical applications. The role demands strong capabilities in prototyping and iterative improvement, bridging cutting models with real‑world applications while maintaining scientific rigor and measurable business impact.

Key job responsibilities

  • Lead the development of sophisticated AI solutions leveraging deep learning, large language models, and advanced machine learning techniques to transform both seller operations and internal consultancy capabilities at scale.
  • 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.
  • Design and implement advanced ML architectures combining multiple learning paradigms—from reinforcement learning and causal inference to predictive modeling—to tackle critical marketplace challenges.
  • Architect next‑generation recommendation and optimization systems that handle complex multi‑dimensional constraints while maintaining robustness and interpretability at scale.
  • 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.
  • Pioneer novel applications of foundation models and generative AI, developing sophisticated evaluation frameworks while maintaining Amazon’s high standards for accuracy and reliability.
  • Lead technical discussions across organizational boundaries, effectively communicating complex scientific concepts to diverse stakeholders while staying at the forefront of ML/AI research advancements.

About The Team

The SAS team aims to accelerate the full potential of our Sellers, helping them to navigate the increasing complexity of the e‑commerce space. Our team provides in‑depth strategic consultancy using a data‑driven, collaborative, and customer‑focused approach to achieve commercial goals of Amazon Sellers.

Basic Qualifications

  • 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.
  • Experience training and deploying machine learning systems to solve large‑scale optimizations.

Preferred Qualifications

  • Experience developing, deploying and managing AI products at scale.
  • Track record of investigating, designing, and delivering innovative ML solutions that drive significant business impact.

Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice to know more about how we collect, use and transfer the personal data of our candidates. Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit for more information. If the country/region you’re applying in isn’t listed, please contact our Recruiting Partner.

Applied Scientist II, Strategic Account Services (SAS) in London employer: Amazon Science

Amazon is an exceptional employer, offering a dynamic and inclusive work culture that fosters innovation and collaboration. As an Applied Scientist II in the Strategic Account Services team, you will have the opportunity to drive cutting-edge AI solutions that directly impact Amazon's global seller base, while benefiting from extensive employee growth opportunities and a commitment to diversity. Located in a vibrant tech hub, Amazon provides a stimulating environment where your contributions are valued and your professional development is supported.

Amazon Science

Contact Details:

Amazon Science Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Applied Scientist II, Strategic Account Services (SAS) in London

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We think you need these skills to ace Applied Scientist II, Strategic Account Services (SAS) in London

Machine Learning
Deep Learning
Large Language Models
Operations Research
Statistics
Prototyping
Iterative Improvement

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