Applied Scientist, SCOT

Amazon • Bellevue, WA

2d

About The Position

Are you looking for an environment where you can drive innovation, apply
state-of-the-art machine learning techniques, and tackle real-world problems at
massive scale? Do you want to shape the future of Amazon’s Retail business by
designing and deploying deep learning models for time series forecasting? If so,
join the Amazon Demand Forecasting Science team! As part of this dynamic team,
you will create and implement novel neural network architectures to develop the
next generation of foundation time series models, directly influencing how
Amazon operates at a global scale.

Requirements

• 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

Nice To Haves

• 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.

Responsibilities

• Design new deep learning algorithms and creative approaches to push time series forecasting performance beyond the current state-of-the-art.
• Work cross-functionally with product managers, software engineers, and scientists to build end-to-end solutions that are deployed at scale in Amazon’s production systems.
• Engage with the broader scientific community at Amazon and beyond - collaborate with academic researchers, contribute to publications, and present your work at leading conferences (e.g., NeurIPS).

Benefits

• health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
• 401(k) matching
• paid time off
• parental leave

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree