Come and help us enable AWS field organization to be most efficient and effective in the world! As a diverse team of economists, research scientists, applied scientists, and engineers, our mission is to support the sales field with tools and analytic solutions, so that they can deliver best-in-class experience for their customers. One particular business-critical area of focus is measuring long-term financial impact of various programs, such as professional services.
As an Economist II on the team, you will contribute to the development of state-of-the-art observational causal inference models, often by combining econometric and machine learning methods, and create the best measure of long-term value that will drive optimal decisions across AWS Professional Services (ProServe) organization. In this process, you will work closely with other economists, product managers, and business stakeholders to map key business-critical questions into econometric solutions, identify relevant data sources, and prototype, test, and put into production respective analytic solutions, leveraging the power of diverse data. These products will lay the foundation of strategic program investments in AWS ProServe, and will ideally inform optimal engagement policy.
You will work in a fast-paced environment and take on challenging and often ambiguous business problems. You will distill business requirements into statistical problems and create elegant solutions that leverage rapidly evolving academic literature at the intersection of causal inference and machine learning. You will code in Python or R, use version control, and uphold high coding standard across the team. You will have access to knowledge and advice of world-renown leaders on causal inference, such as Guido Imbens, David Card, and Victor Chernozhukov, among others.
Key job responsibilities
• Own entire lifecycle of analytic projects, from identifying key data sources, to prototyping and estimating causal models, to testing and validation, to collaborating with engineers for putting solutions into production systems;
• Effectively communicate results and complex scientific ideas to diverse audiences, including tech and non-tech stakeholders, both verbally and by writing clearly and succinctly. Proactively seek diverse perspectives from other scientists and business leaders to improve solutions;
• Work with machine learning experts and time series economists to develop predictive and forecasting tools to enhance core applications of causal inference products.
About the team
Inclusive Team Culture
Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have twelve 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. 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.
Our 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.
Mentorship & Career Growth
Our 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. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded professional and enable them to take on more complex tasks in the future.
- PhD in Economics or closely related field
• 2+ years of experience in industry, research, consulting, or government
• Strong background in applied microeconomics and causal inference
• Demonstrated ability to develop and deliver analytic solutions to real world business problems
• Demonstrated ability to communicate complex scientific solutions to non-technical audiences
• Proficiency in statistical programming languages such as Python, R, or Stata; proficiency in Python or R strongly preferred
• Experience in querying languages such as SQL
• Strong written and verbal communication skills
• Ability to work independently in a fast-paced and ambiguous environment
• Background in machine learning methods, especially at the intersection of causal inference
• Experience with large-scale data-processing systems such as Spark
• Familiarity with software engineering best practices
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.