Description

In Stores Security, we develop authorization solutions using attribute-based access control (ABAC) to enable Amazon to enforce fine-grained access control (FGAC) on sensitive data. We create logging solutions that generate access events from thousands of services for evidence collection. Additionally, we implement rule-based and machine learning systems to monitor access to customer data, detect anomalous activities, and demonstrate compliance.

We are seeking a talented, self-directed Applied Scientist to work on the cutting edge security technologies. You'll design and run experiments, research new algorithms, and find new ways of protecting Amazon's customer trust. Besides theoretical analysis and innovation, you will work closely with talented engineers to put your algorithms and models into practice. You should thrive in ambiguous environments that require to find solutions to problems that have not been solved before. You enjoy and succeed in fast paced environments where learning new concepts quickly is a must. You leverage your exceptional technical expertise, a sound understanding of the fundamentals of Computer Science, and practical experience in building large-scale distributed systems. Your strong communication skills enable you to work effectively with both business and technical partners.


Key job responsibilities
- Process and analyze large data sets using as many techniques as necessary
- Deliver scalable models that can analyze large data sets efficiently
- Build mathematical models to detect and classify specific data elements with high accuracy
- Prototype these models by using high-level modeling languages such as Python. A software team will be working with you to transform prototypes into production.
- Create, enhance, and maintain technical documentation, and present to other scientists and business leaders.

Basic qualifications

- PhD or equivalent Master's Degree plus 4+ years of experience in Computer Science, Machine Learning, Operational Research, Statistics, or a other quantitative field;
- 3+ years of practical experience applying ML to solve complex problems;
- Algorithm and model development experience for large-scale applications;
- Experience using Java, C++, or other programming language, as well as with R or Python;
- Experience distilling informal customer requirements into problem definitions, dealing with ambiguity and competing objectives.

Preferred qualifications

- Practical experience applying ML to solve complex problems;
- Significant peer reviewed scientific contributions in premier journals and conferences;
- Strong fundamentals in problem solving, algorithm design and complexity analysis;
- Experience with defining research and development practices in an applied environment;
- Proven track record in technically leading and mentoring scientists;
- Superior verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts.


Amazon Science (www.amazon.science) gives you insight into the company's approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It's the company's ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists continue to publish, teach, and engage with the academic community, in addition to utilizing our working backwards method to enrich the way we live and work.



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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.