Applied Scientist II, AMZL Learning Product

The Learning & Development Science team in Amazon Logistics (AMZL) builds state-of-the-art Artificial Intelligence (AI) solutions for enhancing leadership and associate development within the organization. We develop technology and mechanisms to map the learner journeys, answer real-time questions through chat assistants, and drive the right interventions at the right time. As an Applied Scientist on the team, you will play a critical role in driving the design, research, and development of these science initiatives.

The ideal candidate will lead the research on learning and development trends, and develop impactful learning journey roadmap that align with organizational goals and priorities. By parsing the information of different learning courses, they will utilize the latest advances in Gen AI technology to address the personalized questions in real-time from the leadership and associates through chat assistants. Post the learning interventions, the candidate will apply causal inference or A/B experimentation frameworks to assess the associated impact of these learning programs on associate performance. As a part of this role, this candidate will collaborate with a large team of experts in the field and move the state of learning experience research forward. They should have the ability to communicate the science insights effectively to both technical and non-technical audiences.

Key job responsibilities
* Apply science models to extract actionable information from learning feedback
* Leverage GenAI/Large Language Model (LLM) technology for scaling and automating learning experience workflows
* Design and implement metrics to evaluate the effectiveness of AI models
* Present deep dives and analysis to both technical and non-technical stakeholders, ensuring clarity and understanding and influencing business partners
* Perform statistical analysis and statistical tests including hypothesis testing and A/B testing
* Recognize and adopt best practices in reporting and analysis: data integrity, test design, analysis, validation, and documentation