Are you passionate about building an experimental culture while creating and delivering differentiated values for our customers? This is a rewarding role where you will be able to draw a clear connection between your work and how it improves customer trust and experience of millions of Amazon customers globally. Help us reinvent risk management to create a resounding impact for our customers.
We are looking for an Applied Scientist to join the Risk Analytics & Insights team and shape our team’s vision, build risk products and execute delivering our big ideas in risk management. In this role, you should be comfortable working independently with the ability to prioritize workloads, remain flexible, and maintain a strong attention to detail in a fast-paced environment. You will evaluate existing and new technologies required for a solution. You will work with a cross-function team (science, product, engineers, risk manager) to apply your specialized expertise to invent, design, and build innovative software that are stable and performant. You will deliver artifacts with the proper level of complexity the first time (or at least minimize incidental complexity). You will make appropriate trade-offs, re-use where possible, and are judicious about introducing dependencies. You are efficient with resource usage (e.g., hardware, data storage, etc.). You will also be involved in the broader scientific community (e.g., give invited talks, lectures, review for conferences).
Key role responsibilities include:
· Research, design and implement scalable machine learning (ML), natural language, or computational models to solve problems that matter to our customers in an iterative fashion.
· Lead the design, implementation, and successful delivery of large, critical, or difficult scientific solutions involving a significant amount of work while delivering solutions with complete independence.
· Map business requirements and customer needs to a scientific problem while providing structure around complex risk decision-making problems.
· Align the research direction to business requirements and make the right judgments on research and development schedule and prioritization
· Contribute to operational excellence in your team’s scientific components, constructively identifying problems and proposing solutions, taking on projects that improve those components, making them better and easier to maintain.
· Mentor and develop junior applied scientists/interns or developers who work on data science and ML/AI problems in the same organization.
· Stay informed on the latest machine learning, natural language and/or artificial intelligence trends and make presentations to the larger engineering and applied science communities.
· PHD/MS. in Computational Linguistics, Economics, Physics, Operational Research, Statistics, Mathematics, Computer Science, Machine Learning, AI or a related quantitative field.
· Minimum of 4-year experience applying ML to solve complex problems after PhD degree or equivalent.
· Minimum of 1-year experience programming in Java, C++, Python or related language
· Experience using deep learning libraries such as TensorFlow or PyTorch, particularly to solve NLP tasks.
· Strong fundamentals in problem solving, algorithm design and complexity analysis.
· Ability to convey rigorous mathematical and science concepts and considerations to non-experts.
· Proficiency in algorithm and model development, model validation and model implementation for large-scale applications and experience using at least one programming languages.
· Experience with defining research and development practices in an applied environment.
· Proven track record in technically leading and mentoring scientists.