Machine Learning Scientist - Applied Scientist, Talent Assessment

Job summary
Amazon's Talent Assessment team ensures the right people are matched to the right roles, quickly, fairly, and with an amazing experience. To achieve this, we design, implement, and optimize hiring systems experienced by millions of candidates annually. We work in a data-rich, 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, efficient, and enjoyable.

Key job responsibilities
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 talent assessment products intended to measure and predict exactly what it requires to be an engaged and successful employee at Amazon.

A day in the life
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.

About the team
We are a team of scientists, and this is an important part of our professional identities. We take our continuing education as well as our contributions to the continuing education of others seriously. To this end, we regularly look for opportunities to engage in reading groups with our peers, present at internal and external conferences, publish our work, and engage in other professional activities in support of our or others development. Learn more about being a scientist at Amazon: https://www.amazon.science.

We put a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life.

We embrace differences and are committed to furthering our culture of inclusion. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.

Basic Qualifications

• 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

Preferred Qualifications

• 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

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, disability, age, or other legally protected status. If you would like to request an accommodation, please notify your Recruiter.