Description Amazon’s global talent is incredibly complex with unique problems to be solved for each line of business. Global Talent Management (GTM) is centrally responsible for managing and evolving Amazon’s human capital through intelligent talent products and processes. GTM Science is a growing interdisciplinary science team within GTM that develops science products and services to facilitate Amazon’s growth and development of talent across all of our businesses and locations around the world. Our vision in GTM Science is to use machine learning and science to scalably solve organizational challenges focused on talent movement, talent differentiation, employee-role matching, promotion processes, organizational design and succession planning, diversity and inclusion, and new areas that address the evolving needs of our diverse employee base. We are looking for an experienced machine learning scientist to work on talent science products that draw from a range of fields such as algorithmic fairness, natural language processing, supervised and unsupervised learning, recommendation systems, machine learning on graphs, reinforcement learning and others on rich and novel datasets. The role has high visibility to senior Amazon business leaders and involves working with other scientists, and partnering with engineering and product teams to integrate these models into production systems. As an applied scientist in GTM Science, you will have the opportunity to work on exciting problems in one of the most innovative applications of science in the People Experience and Technology space. You will help to solve high impact business problems in an unconventional domain, and be encouraged to patent and publish your contributions. If this kind of work excites you, reach out to us to find out more! Key Responsibilities · Design and implement models, science-based product ideas and features, project plans and communicate with stakeholders. · Develop fair predictive models to understand important business and people-centered outcomes · Productionize ML and science models at the scale of Amazon Basic Qualifications - 3+ years of building machine learning models for business application experience - PhD, or Master's degree and 6+ years of applied research experience - Experience programming in Java, C++, Python or related language - Experience with neural deep learning methods and machine learning Preferred Qualifications - Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc. - Experience with large scale distributed systems such as Hadoop, Spark etc. Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits . This position will remain posted until filled. Applicants should apply via our internal or external career site.