Amazon’s Talent Assessment team designs, implements, and optimizes hiring systems for one of the world’s fastest growing companies. We work in a data-focused, global environment solving complex problems with deep thought, large-sample research, and advanced quantitative methods to deliver practical solutions that make all aspects of hiring more fair, accurate, and efficient.
We're looking for a thoughtful applied scientist interested in working on a multi-disciplinary team to create products with a wide audience of users and high business impact in a high regulation environment. In this role, you will apply your skills in collaboration with cross-functional teams of psychologists, UX researchers, engineers, and product managers, to research, develop, analyze and implement new assessment products intended to measure and predict exactly what it requires to be an engaged and successful employee at Amazon.
You will work with a variety of data sources (e.g., structured assessment question responses, unstructured text or audio data, mined data, behavioral data) requiring a breadth of ML knowledge and techniques to deploy scalable algorithms and products. Deployed products must also meet strict fairness and model interpretability standards. You'll be expected to stay informed on the latest machine learning, natural language and artificial intelligence trends.
· PhD or equivalent Master's Degree plus 4+ years of experience in CS, CE, ML or related field
· 2+ years of experience of building machine learning models for business application
· Experience programming in Java, C++, Python or related language
· 3+ years of practical experience applying ML to solve complex problems after PhD degree or equivalent
· Proficiency in algorithm and model development, validation, and implementation for large-scale applications
· Experience with NLP algorithms (e.g., BERT and transformer-based models, topic models) and libraries (e.g. PyTorch, HuggingFace, Tensorflow)
· Experience with cloud-based model and/or container deployment technologies (e.g., AWS Sagemaker, Fargate, ECS, GCP, Azure, Kubernetes, Docker)
· Experience defining research and development practices in an applied environment
· Experience working on fairness in artificial intelligence/machine learning systems, including counterfactual analysis, constrained optimization, and dataset de-biasing
· Ability to convey rigorous mathematical and science concepts and considerations to non-experts.
· Excellent written and oral communication skills