This is a unique opportunity to help shape the workplace experience for Amazonians around the world. The Global Real Estate and Facilities (GREF) organization is responsible for creating the spaces that inspire Amazonians to make history. The globally diverse team manages Amazon’s corporate offices in more than 60 countries. GREF provides real estate transactions expertise, business partnering, space and occupancy planning, design and construction, capital investment program management, facility maintenance, and operations all contributing to the employee experience and leveraging places and programs to enable culture to thrive at Amazon. Within GREF, the Global Customer Insights team represents the voice of the Amazonian throughout the workplace journey. We surface insights that help GREF deliver a workplace experience that enables Amazonians to do their best work while at the office. We are seeking a highly skilled and analytical Research Scientist to join our growing Global Customer Insights team. As a Research Scientist in GREF, you will play an integral part in the measurement and optimization of the Amazon workplace. You will have the opportunity to work with large datasets, including sentiment related data and space utilization sensor data, to enable a holistic understanding of the Amazonian experience and how the office is currently supporting needs. This role is suitable for candidates with strong background in causal inference, statistical analysis, and data-driven problem-solving, with the ability to translate complex data into actionable insights. As a key member of our team, you will work closely with researchers and cross-functional partners to optimize workplace strategies and drive increased customer satisfaction. If you are passionate about deep diving into various data sources to answer challenging questions, enjoy collaborating closely with highly skilled cross-functional partners, and like seeing your efforts translated into real world impact, we want you on our team. Key job responsibilities - Define the data structure, framework, design, and evaluation metrics for research solution development and implementation under minimal guidance. - Design, build, and validate causal models to evaluate the impact of workplace innovation initiatives – encompassing both physical and digital initiatives. Leverage advanced statistical methods to identify and quantify causal relationships. - Design and implement randomized controlled trials (RCTs) to rigorously test the effectiveness of workplace innovations and building level design strategies. Ensure robust experimental design and proper execution to derive credible insights. - Perform complex statistical analyses to interpret data from sentiment-based research programs, experiments, and observational studies. Use statistical software and programming languages to analyze large datasets and extract meaningful patterns. - Work closely with cross-functional teams, including user research, design, space planning, and strategy, to identify key business questions and develop analytical solutions. Foster a culture of data-informed decision-making across the organization. - Keep abreast of the latest developments in data science, causal inference, and space utilization analytics. Apply new methodologies and technologies to improve the accuracy and efficiency of marketing measurement. - Maintain comprehensive documentation of models, experiments, and analytical processes. Prepare summaries that effectively communicate complex analyses to non-technical audiences. About the team This role will be a key lead within the newly formed Global Transformation & Insights org. Key functions within this team include Customer Insights, Business Insights, Creative + UX Design, and Communications. We are a group of builders, creators, innovators and go getters. We are customer obsessed, and index high on Ownership. We Think Big, and move fast, and constantly challenge one another while collaborating on "what else", "how might we", and "how can I help". We celebrate the unique perspectives we each bring to the table. We thrive in ambiguity.
October 21, 2024Causal machine learning provides a powerful tool for estimating the effectiveness of Fulfillment by Amazon’s recommendations to selling partners.
