Amazon Applied Scientist II Selection Monitoring 

India, Karnataka, Bengaluru 

37232724

Selection Monitoring team is responsible for making the biggest catalog on the planet even bigger. In order to drive expansion of the Amazon catalog, we use machine learning and cluster-computing technologies to process billions of products and algorithmically find products not already sold on Amazon. We work with structured, semi-structured and Visually Rich Documents using deep learning, NLP and image processing .You will encounter many challenges, including:
- Scale (build models to handle billions of pages), - Accuracy (requirements for precision and recall)You will help us to
- Build a scalable system which can algorithmically extract information information from world wide web
- Intelligently cluster web pages, segment and classify regions , extract relevant information and structure the data available on semi-structured web pages
- Build systems that will use existing Knowledge Base to perform open information extraction at scale from visually rich documents.Key job responsibilities- Efficiently Crawl web, Automate extraction of relevant information from large amounts of Visually Rich Documents and optimize key processes
- Design, develop, evaluate and deploy, innovative and highly scalable ML models- Establish scalable, efficient, automated processes for large scale model development, model validation and model maintenance
- Leading projects and mentoring other scientists, engineers in the use of ML techniques

- 3+ years of building models for business application experience
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

- Experience using Unix/Linux
- Experience in professional software development
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Master's degree or above in computer science, computer engineering, or related field