RBS Tech is committed to support business growth across WW Retail through standardization, simplification and automation. We are developing an automated solution – ACE – to fix catalog defects. ACE uses Machine Learning/Deep Learning to fix attribute defects identified by Sherlock (auto audit platform) across Amazon’s selection that will improve customer shopping experiences. The defects are reviewed for validation and fixed, in the process helping the machine to learn.
Last year the team worked on discoverability challenges customers are facing on Amazon properties by backfilling hidden attributes by applying various deep learning techniques and an ensemble of classifiers. This year, we are working on highly ambiguous use cases like size variations problems, expanding the scope of discoverability use cases. All these use cases are ideal candidates to apply various Deep learning techniques.
We are hiring applied scientists who can creatively solve these use cases.
As an Applied Scientist in RBS ACE team, you will work with talented peers to develop novel algorithms and modeling techniques to solve these high impact issues. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in artificial intelligence. You will collaborate with other scientists and work in a fast paced team environment. Your work will directly impact our customer shopping experience, save millions in concessions. You will constantly stretch the boundaries of Machine Learning to tackle business challenges.
If you are customer obsessed, self-driven, tenacious and analytical, you will have fun solving our business problems of unprecedented scale. As an experienced machine learning scientist, you will help research and develop new computer algorithms leveraging both classical and deep learning techniques.
· PhDs, specialized in Information Retrieval and Machine Learning.
· Big thinker that can take broad visions and concepts and develop structured plans, actions and measurable metrics and then execute those plans.
Ideal candidate profile
· PhD/Master Degree in Computer Science with experience in Deep Neural Network specialization either in Computer Vision and/or Natural Language Processing.
· Strong skills in problem solving, programming and computer science fundamentals.
· Expertise in using Python programming language.
· Well versed in DeepLearning Frameworks like Tensorflow, MXNet or alternatively front ends like Keras along with numpy, pandas and scikit-learn