Are you seeking an environment where you can drive innovation? Do you want to apply inference, advanced statistical modeling and techniques to solve world's most challenging problems in? Do you want to play a crucial role in the future of Amazon's Retail business? Do you want to be a part of a journey that develops a new technology from scratch for answering critical business question in Amazon Retail?

Every time an Amazon customer makes a purchase, a number of systems are involved: these systems help optimize acquisition, enable a number of purchase options, ensure great , store products so they are available for fast delivery, and minimize package frustration. The Technology (SCOT) Group develops and manages these systems. We are central to Amazon customers' ability to find what they want and get it when they want it.

The SCOT Lab team within SCOT Forecasting is responsible for designing and executing the inference and experimentation systems that measure the impact of SCOT initiatives. We are looking for senior applied scientists to drive innovation in SCOT by developing/building a new scientific approach and pushing our system further upstream in the innovation process. Key responsibilities of a Research Scientist in IPC Lab include:
- Developing new statistical, causal, and machine learning techniques and develop solution prototypes to drive innovation

- Working with technical and non-technical customers to design experiments and communicate results

- Collaborating with our dedicated software team to create production implementations for large-scale data analysis

- Developing an understanding of key business metrics / KPIs and providing clear, compelling analysis that shapes the direction of our business

- Presenting research results to our internal research community

- Leading training and informational sessions on our science and capabilities

- Your contributions will be seen and recognized broadly within Amazon, contributing to the Amazon research corpus and patent portfolio.

To help describe some of our challenges, we created a short video about at Amazon -

Amazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation / Age

- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning

- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.

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.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

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 This position will remain posted until filled. Applicants should apply via our internal or external career site.