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Remote hiring and ML demand increase competition, but specialized NLP and production requirements moderately limit candidate pool.
NLP and ML skills transfer across industries, but domain-specific revenue intelligence product experience raises sensitivity.
Extensive mandatory ML/NLP, production deployment, and specific tooling requirements imply stringent shortlisting.
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Research, develop, and improve machine learning algorithms for natural language processing (NLP) to enhance automation and reduce costs.
Extract and work with large document datasets to build NLP capabilities that improve client service efficiency.
Deploy NLP models in production within a revenue intelligence platform focused on sales productivity and forecasting.
Expertise in NLP/machine learning and deep learning techniques, with knowledge of practical applications and theory.
Proficiency in programming languages such as Python and R, and experience with ML/deep learning frameworks like TensorFlow, Keras, MXNET.
Experience implementing NLP models in production and familiarity with tools like Elasticsearch, Solr, RASA, GPT, and Hugging Face.
Education: graduate or postgraduate degree in Computer Science, Statistics, Analytics, Data Science, Information Systems, or related quantitative field. Work Experience Required: Not explicitly mentioned in the JD.
Senior-level data scientist capable of bridging deep technical NLP research with practical commercial deployment.
Demonstrated ability to communicate complex research to technical and non-technical stakeholders effectively.
Experience working on large-scale NLP projects involving advanced techniques such as conversational AI, reinforcement learning, and knowledge graphs.