As an Applied Scientist, you'll own the definition and implementation of customer-focused, AI-driven innovation in Amazon Customer Service globally, leveraging GenAI, ML, and/or NLP to transform complex business requirements and customer needs into innovative technology solutions. Your expertise will be key in shaping data-driven strategies and addressing complex data challenges. With expertise in AI, text analysis, text embedding, language modeling, and generation, you'll design and develop scalable AI-powered technology solutions, prioritize initiatives, drive data-driven insights, and deliver business impact. This position will advance applied science best practices, leverage AI to drive customer experience (CX) improvements, and set new global standards for customer experience. This role requires working with a cross-functional team, including scientists, engineers, and product managemen,t to develop scalable and maintainable AI solutions for both structured and unstructured data. The ideal candidate has strong technological skills in ML, NLP, and GenAI, excellent written documentation skills, and experience with big data technologies.
You should care deeply about customers and strive to deliver customer value through applied science. You should work well in a fast-paced setting, using your skills to create effective solutions. With a combination of deep business know-how and hands-on skills, you will tackle complex challenges and drive continuous improvement.
Key job responsibilities
- Develop innovative solutions to complex problems, (e.g., extending the functionalities of conversational assistants).
- Apply technical expertise to implement novel algorithms and modeling solutions, in collaboration with other scientists and engineers.
- Analyze data and define metrics to identify actionable insights and measure improvements in customer experience.
- Communicate results and insights to both technical and non-technical audiences through written reports, presentations, and internal and external publications.
- Collaborate with product management and engineering teams to integrate and optimize models in production systems.
Come join a team of bright, passionate, and customer-obsessed individuals who are having fun and making history! We would love to talk to you!
A day in the life
A typical day for an Applied Scientist in the Customer Service science team involves a combination of gaining business know-how and 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 involves monitoring data quality, evaluating model performance, and generating actionable insights from vast amounts of information. By the end of the day, you will have resolved complex technical issues, advanced important AI projects, and conceived new ideas to improve how Amazon utilizes data to transform the customer experience.
If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!
Benefits Summary:
Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include:
1. Medical, Dental, and Vision Coverage
2. Maternity and Parental Leave Options
3. Paid Time Off (PTO)
4. 401(k) Plan
About the team
Our science team is a new function within Customer Engagement Technology. We develop and apply generative AI (GenAI), computer vision, machine learning, ontologies, and natural language processing (NLP) to enhance customer service associate experiences and improve foundational technologies.
