Data Science Manager - AI & ML

About the role

LSEG is looking for an experienced Data Science Manager to lead a team of Data Scientists and ML Engineers building AI solutions for financial markets. The role combines technical leadership, people leadership and strategic execution, driving innovation with LLMs, Generative AI, Deep Learning, Transformers and Agentic AI.

Key responsibilities

• Lead the design, development, evaluation and deployment of production-grade AI and ML solutions, and set the technical vision for scalable, secure and maintainable products.
• Drive innovation in Generative AI, LLMs, Agentic AI, RAG, Deep Learning and Transformer architectures, and evaluate emerging technologies and foundation models.
• Define standard methodologies for LLM evaluation, model benchmarking, AI quality measurement and performance assessment.
• Guide architectural decisions across AI platforms, model-serving infrastructure, data pipelines and MLOps/LLMOps, and partner with Engineering teams to productionize AI solutions.
• Build, mentor and lead Data Scientists and AI/ML practitioners, covering hiring, onboarding, performance management, coaching and career development.
• Partner with Product, Engineering, Research and business leaders on AI strategy, roadmap and priorities, and champion engineering excellence, governance and responsible AI.

Required profile

• Proven track record of leading technical teams and delivering large-scale AI initiatives from concept to production.
• Strong coaching, mentoring and talent development skills, and experience leading multiple concurrent programs.
• Ability to communicate complex technical ideas to technical, business and executive audiences and to engage senior leadership.
• Bachelor's degree in a quantitative field (Computer Science, Data Science, Statistics, Mathematics, Engineering, Physics, AI/ML); Master's or PhD preferred.

Required skills

• Deep expertise in LLMs, Generative AI, RAG, Agentic AI, Deep Learning and Transformer architectures.
• Hands-on experience with LLM evaluation, benchmarking, prompt engineering, guardrails and AI quality assessment.
• Advanced Python and AI frameworks such as PyTorch, TensorFlow, Scikit-Learn, Hugging Face, LangChain and Semantic Kernel.
• Cloud-native AI with Azure AI Foundry, Azure Machine Learning, Azure OpenAI, Azure AI Search, AWS AI Services and modern cloud architectures.
• MLOps and LLMOps, including CI/CD, model lifecycle management and production operations.
• Desirable: experience in financial services, multi-agent systems, knowledge graphs, vector databases, and DevOps, Kubernetes and IaC.