Senior Applied Scientist

- 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 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 with statistical modeling, machine learning, transformer architectures and representation learning
- Experience with designing experimentation and measurement frameworks to evaluate model performance and business impact

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We are seeking a talented, customer-focused Senior Applied Scientist to join the AVS ProServe Science and Data Team. In this role, you will develop and apply machine learning algorithms to solve ambiguous business problems, leading the roadmap to help vendors understand their customers, identify opportunities for growth and define long-term strategy.

The ideal candidate brings deep expertise in machine learning, interpretable models, transformers, and experimentation, along with the business acumen to translate problems into scalable science solutions.

We're looking for a self-starter with an entrepreneurial spirit who is comfortable with ambiguity, demonstrates strong attention to detail, and thrives in a fast-paced, data-driven environment — with a passion for driving measurable impact.

At Amazon, you'll work alongside the latest AI and GenAI tools that are increasingly woven into how teams operate: from AI-powered capabilities that accelerate decision-making, to Generative AI that helps you focus on work that truly matters. You'll have opportunities and resources to develop AI fluency at your own pace, with continuous learning built into the culture.

Key job responsibilities
• Develop customer understanding models by designing and building orchestrated ML solutions that enable vendors to deeply understand their customers and uncover growth opportunities
• Bridge science and business strategy by translating model outputs into actionable insights and recommendations that inform vendor growth strategies, customer acquisition, and long-term planning
• Measure and validate business impact by closing the loop between science solutions and business outcomes — establishing measurement frameworks that quantify impact, surface new opportunities, and continuously refine the path to vendor growth
• Lead cross-functional collaboration by working with engineers, scientists, consultants, and business leaders to deploy scalable solutions while communicating complex technical concepts clearly to non-technical audiences
• Stay at the forefront of innovation by applying state-of-the-art techniques in machine learning, interpretable models, and transformers to solve ambiguous business problems while fostering rapid experimentation and continuous learning