Research Scientist, Intelligent Talent Acquisition

EUR 117.000 - 184.000 Job ID: | Amazon.com Services LLC Do you want a role with deep meaning and the ability to make a major impact? As part of Intelligent Talent Acquisition (ITA), you'll have the opportunity to reinvent the hiring process and deliver unprecedented scale, sophistication, and accuracy for Amazon Talent Acquisition operations. ITA is an industry‑leading people science and technology organization made up of scientists, engineers, analysts, product professionals and more, all with the shared goal of connecting the right people to the right jobs in a way that is fair and precise. Last year we delivered over 6 million online candidate assessments, and helped Amazon deliver billions of packages around the world by making it possible to hire hundreds of thousands of workers in the right quantity, at the right location and at exactly the right time. You’ll work on state‑of‑the‑art research, advanced software tools, new AI systems, and machine learning algorithms, leveraging Amazon's in‑house tech stack to bring innovative solutions to life. Join ITA in using technologies to transform the hiring landscape and make a meaningful difference in people's lives. Together, we can solve the world's toughest hiring problems. Within ITA, the Global Hiring Science (GHS) team designs and implements innovative hiring solutions at scale. We work in a fast‑paced, global environment where we use research to solve complex problems and build scalable hiring products that deliver measurable impact to our customers. We are seeking selection researchers with a strong foundation in hiring assessment development, legally‑defensible validation approaches, research and experimental design, and data analysis. Preferred candidates will have experience across the full hiring assessment lifecycle, from solution design to content development and validation to impact analysis. We are looking for equal parts researcher and consultant, who is able to influence customers with insights derived from science and data. You will work closely with cross‑functional teams to design new hiring solutions and experiment with measurement methods intended to precisely define exactly what job success looks like and how best to predict it. Key job responsibilitiesDesign large‑scale personnel selection research that shapes Amazon’s global talent assessment practices across a variety of topics (e.g., assessment validation, measuring post‑hire impact). Partner with key stakeholders to create innovative solutions that blend scientific rigor with real‑world business impact while navigating complex legal and professional standards. Apply advanced statistical techniques to analyze massive, diverse datasets to uncover insights that optimize our candidate evaluation processes and drive hiring excellence. Explore emerging technologies and innovative methodologies to enhance talent measurement while maintaining Amazon's commitment to scientific integrity. Translate complex research findings into compelling, actionable strategies that influence senior leader/business decisions and shape Amazon's talent acquisition roadmap. Write impactful documents that distill intricate scientific concepts into clear, persuasive communications for diverse audiences, from data scientists to business leaders. Ensure effective teamwork, communication, collaboration, and commitment across multiple teams with competing priorities. A day in the life: Imagine diving into challenges that impact millions of employees across Amazon's global operations. As a GHS Research Scientist, you'll tackle questions about hiring and organizational effectiveness on a global scale. Your day might begin with analyzing datasets to inform how we attract and select world‑class talent. Throughout the day, you'll collaborate with peers in our research community, discussing different research methodologies and sharing innovative approaches to solving unique personnel challenges. This role offers a blend of focused analytical time and interacting with stakeholders across the globe. Basic QualificationsPhD, or Master's degree and 4+ years of quantitative field research experience. Experience investigating the feasibility of applying scientific principles and concepts to business problems and products. Knowledge of data analysis languages (e.g., R, Stata, Python, etc.) or other statistical software. 2+ years of experience in applied selection research, job analysis, test development, and validation. Preferred QualificationsExperience making business recommendations and influencing stakeholders. Knowledge of quantitative data analysis and statistics. PhD in Industrial/Organizational Psychology or related field. Familiarity with using GenAI tools and Large Language Models (LLMs) in personnel selection research. 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 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 $136,000/year in our lowest geographic market up to $212,800/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 . This position will remain posted until filled. Applicants should apply via our internal or external career site. #J-18808-Ljbffr