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Organizational Analytics (OA) Data Scientist (NLP/LLM/GEN AI) - Consultant - Talent & Organization

Salary undisclosed

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YOUR ROLE

Although no two days at Accenture are the same, as an Organizational Analytics (OA) Scientist (NLP) in our Talent & Organization (T&O) practice, a typical day might include:


  • Fetching information from various sources and analyzing it to better understand people behaviors

  • Use of Natural Language Processing (NLP) Algorithm- Static and Dynamic Word Embeddings, Transfer Learning using Deep Learning Framework

  • Working knowledge on development, deployment and prototyping Gen AI / LLM solutions to improve the product landscape

  • Working with cloud platforms and services for GenAI development, such as Azure for using OpenAI models.

  • Train NLP Models for prescribed requirements: Supervised and Unsupervised topic modeling

  • Web Scraping for data mining using state-of-the-art methods

  • Run numeric simulations leveraging different statistic techniques

  • Selecting features, building and optimizing classifiers using machine learning techniques

  • Processing, cleansing, and verifying the integrity of data used for analysis

  • Doing ad-hoc analysis and presenting results in a clear manner

  • Doing custom analytics to deliver insights to clients

  • Contribute to authoring of Thought leadership and research papers

  • Contribute to innovation and new product development in the people and organization analytics space


  • Qualifications


  • Bachelor/Master’s degree in Statistics, Mathematics, Computer Science, Engineer, or Social Sciences

  • 3 to 5 years of experience in Natural Language Processing (NLP), Machine Learning and research

  • 1 year GenAI model/application development experience using methodologies like Retrieval Augmented Generation(RAG), Few shot learning etc.

  • LLM Engineering skills for enabling teams to quickly and efficiently deploy GenAI applications within cloud environment like Azure, AWS etc.

  • Data fluency and working knowledge of statistical methodologies

  • Data interpretation with working knowledge of analytic models and digital tools (coding experience desirable)

  • Fluency in English

  • Ability to perform in a non-structured and dynamic environment

  • Desirable: Code (e.g., Python, R ) developer experience, including writing and testing code, debugging programs and deploying the models or integrating applications with third-party web services


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