Manager, Applied Science, Operations Risk Compliance (ORC) ScienceThe Operations Risk Compliance team enables Amazon worldwide to conduct compliant and safe operations at scale. This is a huge challenge considering the complexity of Amazon's variety of technology to manage in combination with the rapid growth of the company. To accomplish our goals, we are interested in finding top candidates that are ready to take on challenges, are obsessed over customers, and ready to lead the change.
The team automates the manual classification process through technical solutions that rely heavily on machine learning models. We partner closely with business, operations and tech teams worldwide to deliver complex, ambitious roadmaps with the customer's goal in mind. We leverage off the sciences of data, information processing, machine learning, and generative AI to improve user experience, automation, service resilience, and operational efficiency. Our work expands AI-driven capabilities of our Retail businesses.
We are seeking a successful Applied Science Manager to lead our efforts in automating the classification of our entire worldwide selection within regulatory classes. The Applied Science Manager will work with stakeholders from multiple business units to gather requirements and will own our BIE, TPM, Applied Science and Research Science functions to research, develop and deploy the next generation of classification models.
The Applied Science manager will lead a 2-pizzas team comprising Applied Scientists, Machine Learning Engineer, and will be the single threaded owner of high impact Machine Learning solutions that we are building. It is a green field as those solutions still need to be built. The business problems at hand present exciting scientific and engineering challenges, ranging from driving the right research to working under constraint of high throughput inferences. If done well, the impact of the work will be counted in hundreds of millions of dollars.
Beside strong ML knowledge, engineering knowledge and business knowledge, we are looking for candidate with exceptional managerial and communication skills; to be able to lead a high performing cross-functional team to higher successes.
Key job responsibilities Lead an ML product team comprising of Scientists and EngineersAct as a coach and grow the team membersDeliver the research, development and deployment of ML models into production system to automatically classify Amazon's selection within the different regulatory classes.Research and publish new state-of-the-art solutions to improve the efficiency of our manual classification and compliance processes.BASIC QUALIFICATIONS- Master's Degree in computer science, machine learning, operations research, statistics, mathematics or equivalent quantitative field
- Strong mathematical and scientific fundamentals.
- Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers
- Fluency in at least one programming language such as Java, C++ or Python
- 2+ years experience in people management
- Communication skills
PREFERRED QUALIFICATIONS- PhD in related field
- Track record of leading team to deliver several successful products that leverage machine learning to drive value.
- Track record of fostering a positive, inclusive and motivating culture within a team
- Track record of publication in scientific journals
- Knowledge in project management and work methodologies including agile methodologies (e.g. scrum, kanban)
- Good business acumen
Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.
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