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Are you seeking an environment where you can drive innovation? Do you want to apply learning techniques and advanced mathematical modeling to solve real world problems? Do you want to play a key role in the future of Amazon's Retail business? This job for you! The Customer Behavior Analytics (CBA) team is looking for motivated individuals with strong ML and analytical skills. The CBA team is responsible for the architecture, design and implementation of tools used to understand customer behavior, estimate the Economic value of Amazon programs like Prime, and guide program teams on prioritization/investment/policy optimization decisions. Come and join us!

Amazon’s CBA team is looking for Applied Scientists, who can work at the intersection of machine learning, statistics and economics; and leverage the power of big data to solve complex problems like long-term causal effect estimation.

As an applied scientist, you will bring statistical modeling and machine learning advancements to analyze data and develop customer-facing solutions in complex industrial settings. You will be working in a fast-paced, cross-disciplinary team of researchers who are leaders in the field. You will take on challenging problems, distill real requirements, and then deliver solutions that either leverage existing academic and industrial research, or utilize your own out-of-the-box pragmatic thinking. This role requires a pragmatic technical leader comfortable with ambiguity, capable of summarizing complex data and models through clear visual and written explanations. The ideal candidate will have experience with machine learning models (generative models and graphical analysis) and causal inference. Additionally, we are seeking candidates with strong rigor in applied sciences and engineering, creativity, curiosity, and great judgment.

Your responsibilities include:

• Understand and mine the large amount of data, prototype and implement new learning algorithms and prediction techniques to improve long-term causal estimation approaches.

• Collaborate with product managers and engineering teams to design and implement solutions for Amazon problems

• Design, build, and deploy effective and innovative ML solutions to improve various components of our ML and causal inference pipelines

• Publish and present your work at internal and external scientific venues in the fields of ML and causal inference.

Your benefits include:

• Working on a high-impact, high-visibility product, with your work improving the experience of millions of customers.

• The opportunity to use (and innovate) state-of-the-art ML methods to solve real-world problems.

• Excellent opportunities, and ample support, for career growth, development, and mentorship.

Basic Qualifications

• PhD in econometrics, statistics, industrial engineering, operations research, optimization, data mining, analytics, or equivalent quantitative field

Preferred Qualifications

• Experience in applied research

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.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/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 https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.