Amazon Applied Scientist Customer Engagement Technology 

United States, Washington, Seattle 

33637478

Key job responsibilities
- Develop innovative solutions to complex problems (e.g., extending the functionalities of conversational assistants ).
- Communicate results and insights to both technical and non-technical audiences through written reports, presentations, and both internal/external publications.
A day in the life
A typical day for an Applied Scientist in the Customer Service's Data and AI team involves a combination of gaining business know-how with hands-on problem-solving in NLP and GenAI. You'll tackle complex data initiatives, ensuring alignment with customer needs and business objectives, and translate business requirements into practical AI-driven solutions. Collaborating with cross-functional teams, you'll design and enhance AI models, focusing on efficiency, precision, and scalability. Your day will involve ensuring data quality, model performance, and generating actionable insights from vast amounts of information. By the end of the day, you will have typically resolved complex technical issues, advanced important AI projects, and conceived new ideas to improve how Amazon utilizes data to transform the customer experience.Benefits Summary:1. Medical, Dental, and Vision Coverage
2. Maternity and Parental Leave Options
3. Paid Time Off (PTO)
4. 401(k) Plan

- 3+ years of building models for business application experience
- Master's degree in computer science, machine learning, engineering, or related fields

- Advanced degree (PhD) in Computer Science, Machine Learning, Engineering, or a related quantitative field.
- Hands-on experience developing and deploying GenAI applications, such as language models, text classification.
- Experience programming in Java, C++, Python or related language
- Strong understanding of GenAI or NLP fundamentals.
- Proficiency in designing and implementing large-scale data pipelines, and cloud-based data infrastructure.
- Proven track record of tackling highly ambiguous, complex data challenges and delivering impactful solutions.