Manager - Research Scientist, AWS Workforce Planning

San Francisco, California, USA

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Job summary
AWS Workforce Planning Product, Engineering and Science team is looking for a Research Scientist Manager with expertise in applying causal inference, experimental design, or causal machine learning techniques to topics in labor, personnel, education, health, public, or behavioral science. We are particularly interested in candidates with experience applying these skills to strategic problems with significant business and/or social policy impact.

This Manager will lead a small team that will work with economists, product managers and engineers to estimate and validate their models on large scale data, and will help business partners turn the results of their analysis into policies, programs, and actions that have a major impact on Amazon’s business and its workforce. We are looking for creative thinkers who can combine a strong scientific toolbox with a desire to learn from others, and who know how to execute and deliver on big ideas.

Key job responsibilities
You will conduct, direct, and coordinate all phases of research projects, including defining key research questions, developing models, designing and implementing appropriate data collection methods, executing analysis plans, and communicating results.

You will earn trust from our business partners by collaborating with them to define key research questions, communicate scientific approaches and findings, listen to and incorporate their feedback, and deliver successful solutions.

About the team
The AWS Workforce Planning Product, Engineering and Science team has the mission to put the right people in the right place at the right time to staff all of AWS, building scalable and robust software solutions that plan the AWS workforce and support execution to plan.

We harness economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of product, science and engineering to develop and deliver solutions that measurably achieve this goal.

Basic Qualifications


  • Master's degree
  • 5+ years of research experience in a quantitative field
  • Experience investigating the feasibility of applying scientific principals and concepts to business problems and products

Preferred Qualifications

  • Ph.D. in Computer Science, Machine Learning, Operational Research, Statistics or a related quantitative field
  • Experience leading others
  • 5+ years of research or work experience as a Research Scientist, Research Assistant, Software Engineer, or a related occupation.
  • Superior verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts
  • 2+ years of research or work experience in programming with a mathematical programming language such as R, MATLAB, or SAS or major programming language such as Python, Java, C++, C#, or C
  • Experience formulating and solving predictive modeling, machine learning, forecasting or statistical modeling problems.


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.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰

Tags: AWS Causal inference Computer Science Economics Engineering Machine Learning Matlab Predictive modeling Python R Research SAS Statistical modeling Statistics

Region: North America
Country: United States
Job stats:  1  1  0

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