Analytical and Problem solving, Data Analytics, Data Mining, Data science techniques, Deep Learning, Design, Java Programming, Machine learning techniques, Python Programming, SQL
Amazon operates in a virtual, global eCommerce environment in seven countries across the Globe. Every day, millions of customers rely on Amazon to give them access to one of the world's largest selections of consumer goods. To continue to delight and exceed our customer's expectations, at Amazon, we take the quality of our catalog very seriously. That's where you can help. The RBS group provides catalog augmentation and correction technologies for the Amazon selling community. Our solutions ensure information in Amazon's catalogs is both complete and comprehensive enough to give our customers a great shopping experience every time.
We are looking for a customer obsessed Data Scientist who can apply the latest research, state of the art algorithms and machine learning to build highly scalable systems in the e-commerce domain.
As a successful Data Scientist in Walk the Store, you will work on variations, a concept, which consists of a family of product detail pages that vary by specific product attributes. You will navigate through numerous unexplored areas of product variation, driven by the diversity of millions products and across essential variation components within the amazon eco system, to formulate scaleable mechanisms that deliver superior customer experience. To be successful in this role, you need to be a sophisticated user of advanced data extraction and transformation tools (e.g Python, SQL), and will need to understand the source data and be able to synthesize it down to a form suitable for answering specific business questions, and machine learning. You should be able to model common patterns within the product families using unstructured data, design algorithms to detect anomalies at attribute level which are highly dynamic and differs by product. Work with higher dimensional features, apply scalable decomposition methods and drive feature singularity across the product type. Consider any anomaly in the product attribute is a defect and leverage Self-supervised algorithms to analyze anomalies in product attributes and recommend on possible fixes. You will need to be an expert in sequence modelling, with hands on experience on embedding and localized anomaly detection models. You need to be imaginative with a flair to solve complex problems in a challenging environment with a passion to build solutions that improve the experience of millions of Amazon customers. You will also need to be an expert at communicating insights and recommendations to audiences of varying levels of technical sophistication.
As an experienced research analyst, you will help/mentor other research analyst and develop new algorithms leveraging both classical and deep learning techniques.
Key Responsibilities for this Role:
Key Performance Areas:
BASIC QUALIFICATIONS
PREFERRED QUALIFICATIONS
Amazon.com, Inc. is an American multinational technology company with operations in cloud computing, streaming media, artificial intelligence, and e-commerce. The company has been referred to as one of the most influential economic and cultural forces in the world, and it is one of the world's most valuable brands.
Vancouver, BC, Canada
2-4 year
Toronto, ON, Canada
2-4 year
Vancouver, BC, Canada
2-4 year
Toronto, ON, Canada
2-4 year
Vancouver, BC, Canada
2-4 year
Berlin, Germany
2-4 year
Arlington, VA, USA
2-4 year
Austin, TX, USA
2-4 year
Bellevue, WA, USA
2-4 year
California City, CA, USA
2-4 year
Newark, NJ, USA
2-4 year
Newark, NJ, USA
2-4 year
Seattle, WA, USA
2-4 year
Seattle, WA, USA
2-4 year
Seattle, WA, USA
2-4 year
Seattle, WA, USA
2-4 year
Seattle, WA, USA
2-4 year
Seattle, WA, USA
2-4 year
Sumner, WA, USA
2-4 year
Seattle, WA, USA
2-4 year
Mumbai, Maharashtra, India
2-4 year
Berlin, Germany
2-4 year
New York, NY, USA
2-4 year
New York, NY, USA
2-4 year
New York, NY, USA
2-4 year
New York, NY, USA
2-4 year
Westborough, MA, USA
0-2 year
Arlington, TX, USA
2-4 year
Seattle, WA, USA
6-8 year
Sunnyvale, CA, USA
6-8 year
New York, NY, USA
6-8 year
Seattle, WA, USA
4-6 year
Palo Alto, CA, USA
0-2 year
New York, NY, USA
0-2 year
Seattle, WA, USA
0-2 year
Arlington, VA, USA
0-2 year
New York, NY, USA
4-6 year
Arlington, VA, USA
4-6 year
Seattle, WA, USA
4-6 year
San Diego, CA, USA
4-6 year
Irvine, CA, USA
4-6 year
San Francisco, CA, USA
4-6 year
Brisbane QLD, Australia
0-2 year
Adelaide SA, Australia
0-2 year
Canberra ACT, Australia
0-2 year
Vancouver, BC, Canada
4-6 year
Vancouver, BC, Canada
2-4 year
San José Province, San José, Costa Rica
0-2 year
Palo Alto, CA, USA
0-2 year
Seattle, WA, USA
0-2 year
Vancouver, BC, Canada
2-4 year
Toronto, ON, Canada
0-2 year
Seattle, WA, USA
8-10 year