Do you want to make a real difference to real people's lives? Want to design and build fair and explainable systems which automate recruitment processes across Amazon? Come and be part of a team that develops new machine learning (ML) technologies, which help Amazon scale for its customers by recruiting diverse teams. You will work as an Applied scientist in a team of other scientists and software developers. You will primarily be writing solutions in Python and will be using the latest technologies including AWS (e.g. Sagemaker). You will be contributing regularly to the code base as this is an applied role with the expectation of 50% of your time spent on the code. Your solutions will meet remarkably high standards of performance and reliability, and will operate at massive scale. You will work as part of a sustainably paced agile team. You will play a hands on leadership role in your team giving you the responsibility, authority, and autonomy to ensure success. You will be involved in every aspect of the process - from idea generation, customer engagement, business analysis and scientific design through to software development and operations. Join a team full of talented people who come from all over the world. Enjoy the chance to work in a relaxed setting with a good social life. The team, primarily based in Edinburgh, Scotland, is rapidly expanding. We are looking for Applied scientists who can delight our customers by continually learning and inventing. Our ideal candidate is an experienced Applied scientist who has a track-record of statistical analysis and building models to solve real business problems, who has great leadership and communication skills, and who has a passion for fairness and explainability in ML systems. The role offers an exceptional opportunity for growth and to make a real difference to Amazon recruitment. If you are selected, you have the opportunity to really impact our business by inventing, improving, and building world class systems, delivering results, working on exciting and challenging projects.