Job summaryAmazon is looking for an outstanding Data Scientist to join the AWS Product Analytics and Data Science team. This is your opportunity to be a core member of the AWS Product Analytics team that has direct impact on the long-term roadmap of the AWS Product teams. This role is within a broader Data science, Business Intelligence & Data engineering team that focuses on using statistical modeling and machine learning to drive actionable and impactful business decisions across AWS. Since early 2006, AWS has provided companies of all sizes with an infrastructure platform in the cloud. AWS is a high-growth, fast-moving division within Amazon with a start-up mentality where new and diverse challenges arise every day.On the AWS Product Analytics team, you will be surrounded by people who are exceptionally talented, bright, and driven, and believe that data-driven decision making is critical to our success. To be successful in this role, you have a strong passion for analytics and accountability, set high standards with a focus on superior business outcome. You should also have strong business acumen who feels comfortable tackling ambiguous business problems in dynamic business environment. Your decision will influence AWS VP and Director level product and business decisions that directly impact AWS’ product roadmap and customer experience.We take working hard, having fun, and making history seriously. AWS sets the standard for functionality, cost, and performance for many cloud-based services, but it’s still early days for cloud computing, and there are boundless opportunities to continue to redefine the world of cloud computing - come help us make history!Key job responsibilitiesThe successful candidate will have a strong quantitative background and can thrive in an environment that leverages statistics, machine learning, operations research, econometrics, and strong business acumen. As a Data Scientist, you will discover and solve real world problems by analyzing one of the world’s largest datasets, developing statistical and machine learning models to drive business decisions, adopting best practices, and conducting data science research and development. You will also collaborate closely with business leaders, software engineers, economists, and researchers.You will adopt the best practices for delivering high quality data science projects, influencing analytics roadmap, setting best practicesYou will work on high visibility and high business impact problems that directly influence AWS product roadmaps and business decisions. You will spend time formulating and defining science problem based on business requirements.You will translate business problems into analytical framework and form testing hypotheses that can be answered with available data using scientific methods or identify additional data neededYou will work on a diverse set of analytics problems, such as user growth, pricing, forecasting, causal inference, marketing research, experimentation, and other machine learning problemsYou will collaborate with cross-function teams with AWS business leaders, data engineers, software engineers, economists, and other scientistsYou will help define key performance indicators for key business programs and propose analytics framework that drive business valueYou are familiar with state-of-the-art statistical and machine learning methods, understand the full life-cycle of analytics projects, have hands-on experience building end-to-end analytics solutionsInclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. 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.Work/Life BalanceOur team puts 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 believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.This position involves on-call responsibilities, typically for one week every two months. We don’t like getting paged in the middle of the night or on the weekend, so we work to ensure that our systems are fault tolerant. When we do get paged, we work together to resolve the root cause so that we don’t get paged for the same issue twice.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.A day in the lifeYou will work directly with internal stakeholders including, product managers, go-to-market leaders, and other business leaders at manager and director level. You will work with customer face-to-face by decompose their ambiguous business problem into a science problem. You will adopt best practices and hold high standard for the team. You will test different business hypotheses and build statistical, machine learning, and causal inference models. You will solve the most impactful and challenging business today to provide actionable insights.About the teamWe are trusted analytics thought partner to enable AWS product and business leaders to make informed product decisions and drive revenue growth, through rigorous science methods, reusable analytics frameworks, and scalable analytics products.