We are a new team in AWS' Kumo organisation - a combination of software engineers and AI/ML experts.
Kumo is the software engineering organization that scales AWS’ support capabilities. Amazon’s mission is to be earth’s most customer-centric company and this also applies when it comes to helping our own Amazon employees with their everyday IT Support needs. Our team is innovating for the Amazonian, making the interaction with IT Support as smooth as possible. We achieve this through multiple mechanisms which eliminate root causes altogether, automate issue resolution or point customers towards the optimal troubleshooting steps for their situation. We deliver the support solutions plus the end-user content with instructions to help them self-serve. We employ machine learning solutions on multiple ends to understand our customer's behavior, predict customer's intent, deliver personalized content and automate issue resolution through chatbots.
As an applied scientist on our team, you will help to build the next generation of case routing using artificial intelligence to optimize business metric targets addressing the business challenge of ensuring that the right case gets worked by the right agent within the right time limit whilst meeting the target business success metric. You will develop machine learning models and pipelines, harness and explain rich data at Amazon scale, and provide automated insights to improve case routing that impact millions of customers every day. You will be a pragmatic technical leader comfortable with ambiguity, capable of summarizing complex data and models through clear visual and written explanations.
About AWS
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
Why AWS?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness.
Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
Sales, Marketing and Global Services (SMGS)
AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector.
Amazon knows that a diverse, inclusive culture empowers us all to deliver the best results for our customers. We celebrate diversity in our workforce and in the ways we work. As part of our inclusive culture, we offer accommodations during the interview and onboarding process. If you’d like to discuss your accommodation options, please contact your recruiter, who will partner you with the Applicant-Candidate Accommodation Team (ACAT). You may also contact ACAT directly by emailing acat-africa@amazon.com. We want all Amazonians to have the best possible Day 1 experience. If you’ve already completed the interview process, you can contact ACAT for accommodation support before you start to ensure all your needs are met Day 1.
Key job responsibilities
Deliver real world production systems at AWS scale.
Work closely with the business to understand the problem space, identify the opportunities and formulate the problems.
Use machine learning, data mining, statistical techniques, Generative AI and others to create actionable, meaningful, and scalable solutions for the business problems.
Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation
Analyze complex support case datasets and metrics to drive insight
Design, build, and deploy effective and innovative ML solutions to optimize case routing
Evaluate the proposed solutions via offline benchmark tests as well as online A/B tests in production.
Drive collaborative research and creative problem solving across science and software engineering team
Propose and validate hypothesis to deliver and direct our product road map
Work with engineers to deliver low latency model predictions to production
We are open to hiring candidates to work out of one of the following locations:
Cape Town, ZAF
Kumo is the software engineering organization that scales AWS’ support capabilities. Amazon’s mission is to be earth’s most customer-centric company and this also applies when it comes to helping our own Amazon employees with their everyday IT Support needs. Our team is innovating for the Amazonian, making the interaction with IT Support as smooth as possible. We achieve this through multiple mechanisms which eliminate root causes altogether, automate issue resolution or point customers towards the optimal troubleshooting steps for their situation. We deliver the support solutions plus the end-user content with instructions to help them self-serve. We employ machine learning solutions on multiple ends to understand our customer's behavior, predict customer's intent, deliver personalized content and automate issue resolution through chatbots.
As an applied scientist on our team, you will help to build the next generation of case routing using artificial intelligence to optimize business metric targets addressing the business challenge of ensuring that the right case gets worked by the right agent within the right time limit whilst meeting the target business success metric. You will develop machine learning models and pipelines, harness and explain rich data at Amazon scale, and provide automated insights to improve case routing that impact millions of customers every day. You will be a pragmatic technical leader comfortable with ambiguity, capable of summarizing complex data and models through clear visual and written explanations.
About AWS
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
Why AWS?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness.
Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
Sales, Marketing and Global Services (SMGS)
AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector.
Amazon knows that a diverse, inclusive culture empowers us all to deliver the best results for our customers. We celebrate diversity in our workforce and in the ways we work. As part of our inclusive culture, we offer accommodations during the interview and onboarding process. If you’d like to discuss your accommodation options, please contact your recruiter, who will partner you with the Applicant-Candidate Accommodation Team (ACAT). You may also contact ACAT directly by emailing acat-africa@amazon.com. We want all Amazonians to have the best possible Day 1 experience. If you’ve already completed the interview process, you can contact ACAT for accommodation support before you start to ensure all your needs are met Day 1.
Key job responsibilities
Deliver real world production systems at AWS scale.
Work closely with the business to understand the problem space, identify the opportunities and formulate the problems.
Use machine learning, data mining, statistical techniques, Generative AI and others to create actionable, meaningful, and scalable solutions for the business problems.
Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation
Analyze complex support case datasets and metrics to drive insight
Design, build, and deploy effective and innovative ML solutions to optimize case routing
Evaluate the proposed solutions via offline benchmark tests as well as online A/B tests in production.
Drive collaborative research and creative problem solving across science and software engineering team
Propose and validate hypothesis to deliver and direct our product road map
Work with engineers to deliver low latency model predictions to production
We are open to hiring candidates to work out of one of the following locations:
Cape Town, ZAF