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Research Scientist

Location Bengaluru, India
Posted 01-September-2020
Position Details: Research Scientist Location: Bengaluru, Karnataka

Openings: 1

Salary Range: 2500000


Job Functions:

Review published literature, conceptualize novel algorithms, implement, evaluate and facilitate deployment of solutions for speech, natural language and dialog problems encountered in human conversations and analytics.
Develop rich models for specific tasks by collecting, curating, coordinating annotations of spoken conversations, training and adapting machine learning models
Tune model performance through feature engineering to optimize model performance by combining rules and machine learning techniques
Integrate the developed models into Curo software and deploy them on Interactions Platforms.Document work through conference publications, file patent disclosures.Mentor junior associates as required.

Experience and Skills required:

MS or PhD degree in Computer Science/Statistics with experience in Machine Learning
Proven success in applying Machine Learning models to practical problems
Understanding of word & sentence representations like Word2Vec, Glove, Bert, ELMO etc
Good understanding of pattern recognition algorithms like k-means, SVM, HMM, GMM, Neural Networks, Viterbi decoding etc.

Expertise in Python
Experience contributing to research efforts, including publishing in conferences
Ability to demonstrate Interactions Values of:

Being passionate about customer service

Obsessing with our customers success

Respecting each other

Creating opportunity

Embracing disruption

Doing what we say we will do


Experience working with machine learning tools, speech recognition tools, web crawlers, finite state machines, and open source natural language toolkits are a plus.
Experience working with deep learning toolkits like PyTorch, Tensor flow etc.
Experience working with Pandas
Experience in natural language processing technologies and services with emphasis in one or more of the following: o Data acquisition and NL modeling: harvesting NL resources from the Web, rapid bootstrapping of domain-specific and multilingual NL models for named-entity, syntactic parsing and text classification NL systems: large-scale development and deployment, performance monitoring, tuning and optimization of NL models
NL methodology: grammar-based, data-driven and machine learning-based, hybrid approaches o NL technologies: spoken language understanding, language translation, natural language search, syntax-semantics.

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