Data Scientist III

at  LexisNexis

Job Responsibilities:

• Develop and implement advanced machine learning models with a focus on Retrieval-Augmented Generation (RAG) systems.
• Design, test, and optimize prompt engineering techniques to enhance the performance of language models.
• Build, fine-tune, and deploy BERT (Bidirectional Encoder Representations from Transformers) models for various NLP tasks.
• Collaborate with cross-functional teams to integrate RAG and BERT models into production systems.
• Conduct experiments to evaluate model performance, identify areas for improvement, and implement enhancements.
• Stay up-to-date with the latest research and developments in the field of natural language processing (NLP) and machine learning.
• Document methodologies, processes, and findings to support knowledge sharing and reproducibility.Proficiency in using genAI for drafting and summarization of documents

Additional Skills and Requirements:

• Proficiency in Python.
• Excellent communication skills.
• Strong problem-solving skills.
• Ability to work in a mono-repo environment with the highest standards.

LexisNexis, a division of RELX, is an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law. We are committed to providing a fair and accessible hiring process. If you have a disability or other need that requires accommodation or adjustment, please let us know by completing our Applicant Request Support Form:
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• Develop and implement advanced machine learning models with a focus on Retrieval-Augmented Generation (RAG) systems.
• Design, test, and optimize prompt engineering techniques to enhance the performance of language models.
• Build, fine-tune, and deploy BERT (Bidirectional Encoder Representations from Transformers) models for various NLP tasks.
• Collaborate with cross-functional teams to integrate RAG and BERT models into production systems.
• Conduct experiments to evaluate model performance, identify areas for improvement, and implement enhancements.
• Stay up-to-date with the latest research and developments in the field of natural language processing (NLP) and machine learning.
• Document methodologies, processes, and findings to support knowledge sharing and reproducibility.Proficiency in using genAI for drafting and summarization of document