Amazon Early Career Applied Scientist Customer Forecasting Valuation 

United States, Washington, Seattle 

517538481

Applied Scientists in CBA work collaboratively with other Scientists and Engineers across teams to build causal and machine learning models to support business decision making. You will contribute to and maintain a highly scalable data and modeling pipeline infrastructure, and also work closely with business stakeholders to translate requirements into ML and software products. We are looking for individuals with a strong interest in learning and researching new methodologies to maximize customer impact in a highly ambiguous and challenging problem space. Prior experience with building machine learning and AI systems is required, as is a solid understanding of the fundamentals of statistics and standard methods in ML and deep learning. Candidates should possess strong software engineering skills and several years of industry experience. Experience with distributed computing environments, AWS, Spark/PySpark, and prior domain experience in causal inference and experimentation is highly preferred.
Diverse Experiences
We value diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.Work/Life Balance

- Experience building models for business application experience
- PhD, or Master's degree, and CS, CE, ML or related field experience
- Experience programming in Python or related languages
- Experience with distributed computing frameworks such as Spark
- Experience in the following areas: algorithms and data structures, numerical optimization, high-performance computing, inferential/Bayesian statistics

- Experience in professional software development
- Experience with generative AI, LLMs
- Experience with large-scale statistical modeling, causal inference, causal machine learning and applied econometrics
- Patents or publications at top-tier peer-reviewed conferences or journals