Data Analytics Lead

The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.

Senior AI Scientist

Company Overview
A globally integrated risk and people solutions provider that leverages analytic insights and specialized expertise to support client decisions across 120+ countries. Dedicated to enriching lives and protecting businesses, this organization prioritizes impactful solutions in human capital, risk management, and financial services.

Key Job Responsibilities

• Solution Development: Design, develop, and deploy natural language processing (NLP) and large language models (LLMs) solutions specifically for finance and insurance applications.
• Research and Innovation: Conduct original research, publish findings, and integrate cutting-edge NLP and LLM advancements into products and services.
• Industry Collaboration: Build connections with academia, technology sectors, and third-party industry partners.
• Best Practices Establishment: Set and maintain standards for NLP and LLMs development and evaluation.
• Client and Stakeholder Communication: Present solutions, explain business value, and align with senior management, clients, and partners.

Unique Aspects of This Role
Working closely with Senior Tech Leadership, you will contribute to pioneering AI products and projects that have a tangible impact across the global organization.

Essential Skills and Qualifications

• PhD or Master’s in Computer Science, AI, Machine Learning, or a related field.
• Proven experience in NLP and LLMs with a strong record of publications, patents, or contributions to open-source projects.
• Proficient in Python and familiar with NLP and LLMs applications in finance and insurance, including areas like text analysis, information extraction, and conversational agents.
• Skilled in advanced AI model architectures and training methods, with knowledge of GPT, Transformers, Siamese-twin models, VAE, GANs, Diffusion, and self-supervision learning techniques.
• Experience in debugging and adapting machine learning systems across varied environments, with specific expertise in Transformers, prompt engineering, and fine-tuning of NLP models.
• Passionate about innovation and problem-solving within NLP and LLMs.

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