Senior Applied Scientist
Company Name:-
ADCI – BLR 14 SEZ
Job Location:-
Bengaluru, Karnataka
Job Summary:-
5+ years of relevant applied science research experience with PhD or 8+ years of experience of applied research experience with a Masters degree in Electrical Engineering, Computer Science, Computer Engineering, Mathematics, or related field with specialization in machine learning, NLP, Computer Vision, deep or related fields.
Should be an expert in either Computer Vision or NLP and have a good understanding of the both.
Strong working knowledge of programming languages such as C/C++, Java, or Python (SciPy, RPy2, etc).
Proven ability to relate to and solve business problems through machine learning, data mining and statistical algorithms
Proven track record in technically leading and mentoring scientists
Published and/or presented papers at ACL, ICML, NIPS, KDD, CVPR or similar top-tier conferences and events.
Technical fluency; comfort understanding and discussing architectural concepts and algorithms, schedule tradeoffs and new opportunities with technical team members.
Strong problem solving skills
Excellent communication skills
RBS (Retail Business Services) Tech team works towards enhancing the customer experience (CX) and their trust in product data by providing technologies to find and fix Amazon CX defects at scale.
Our platforms such as Sherlock for auto-audit, ACE for auto-corrections, and Maestro for optimal manual corrections enable defect audits in all phases of customer journey, including selection, discoverability & fulfilment, buying experience and post-buying experience (product quality and customer returns), and further enable corrections of the identified defects.
As a Sciences team in RBS Tech, we focus on foundational ML research and develop scalable state-of-the-art ML solutions to solve the problems covering customer experience (CX) and Selling partner experience (SPX).
We work to solve problems related to multi-modal entity extraction and slot tagging for Catalog completeness and consistency, supervised and unsupervised techniques like multi-task learning, multi-label classification, aspect and topic extraction for Customer Anecdote Mining, image and text similarity and retrieval using NLP and Computer Vision techniques for grouping similar products and identifying inconsistent famili
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