Data Science(Gen AI & LLM) job opening at GlobalData

Roles and Responsibilities :- As a Data Scientist in the Financial Markets team, you will be responsible for developing cutting-edge AI-driven models and insights for financial markets clients such as Hedge Funds, Private Equity firms, Investment Banks, and Asset Managers. The models you build will leverage both traditional and alternative data sources, to enhance investment strategies. You will work closely with experts in financial markets to:- Develop and deploy AI-driven analytics utilizing proprietary GlobalData datasets to extract actionable insights. Design, fine-tune, and deploy Generative AI models (LLMs, diffusion models, AI agents) for financial market analysis, report generation, and predictive modeling. Implement Retrieval-Augmented Generation (RAG) frameworks to enhance financial document processing and knowledge extraction. Create AI-powered financial agents for automated decision-making and real-time analysis Build and optimize machine learning models for forecasting, scorecards, and predictive analytics. Conduct research on state-of-the-art Generative AI and machine learning techniques, integrating them into financial modelling use cases. Analyze alternative datasets, including textual, sentiment, and transactional data, to generate high-value investment insights. Present findings and Proof-of-Concept (PoC) solutions to senior management, stakeholders, and cross-functional teams. Develop technical documentation and presentation decks to showcase methodologies and outcomes. Job Qualifications Academic Qualification: Bachelor's/Masters degree in Data Science, Computer Science, AI, or a related field. Strong expertise in machine learning and artificial intelligence, particularly in Generative AI applications, NLP, and predictive modelling. Hands-on experience with Large Language Models (LLMs) such as OpenAI GPT, Claude, Gemini, or open-source alternatives (Llama, Mistral, Qwen, etc.). Experience in fine-tuning models, prompt engineering, and AI-driven automation. Proficiency in Retrieval-Augmented Generation (RAG) techniques for information retrieval and knowledge-based AI applications. Solid understanding of traditional analytics techniques, including data modeling, forecasting, and risk scorecards. Proficiency in Python, Tensor Flow/ PyTorch, Lang Chain, and Vector Databases. Experience with cloud platforms (AWS, Azure, GCP) for AI/ML deployments. Familiarity with financial markets is preferred but not essential. Ability to translate research into scalable AI-driven financial solutions.