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LSEG

Global financial market infrastructure and data

Data Science Manager

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeNo salary listed

About the Job

Requirements

  • A bachelor's degree or equivalent experience in Computer Science, Data Science, Statistics, Mathematics, Engineering, Physics, Artificial Intelligence, Machine Learning, or a related quantitative field.
  • A proven track record of building, leading, and developing high-performing teams of Data Scientists, ML Engineers, and AI practitioners.
  • Demonstrated success delivering large-scale AI initiatives from ideation to production with measurable business impact.
  • Strong coaching, mentoring, performance management, and talent development capabilities.
  • Experience leading multiple concurrent programs, balancing priorities, resources, and customer expectations.
  • Experience engaging senior leadership and executive collaborators.
  • Extensive experience designing, building, and deploying production-grade AI and Machine Learning solutions at scale.
  • Deep expertise in LLMs, Generative AI, RAG, Agentic AI, Deep Learning, Neural Networks, and Transformer architectures.
  • Hands-on experience with LLM evaluation, benchmark design, model validation, prompt engineering, guardrails, and AI quality assessment.
  • A good foundation in statistics, probability, optimization, experimentation, and applied machine learning.
  • Advanced proficiency in Python and modern AI frameworks, including PyTorch, TensorFlow, Scikit-Learn, Hugging Face, LangChain, and Semantic Kernel.
  • Experience building and scaling enterprise AI platforms, ML infrastructure, and intelligent applications serving thousands of users.
  • A strong understanding of model observability, monitoring, evaluation frameworks, experimentation, reliability, and operational excellence.
  • Expertise in cloud-native AI development using Azure AI Foundry, Azure Machine Learning, Azure OpenAI, Azure AI Search, AWS AI Services, and modern cloud architectures.
  • Experience implementing MLOps and LLMOps practices, including CI/CD, model lifecycle management, governance, and production operations at scale.
  • Ability to communicate complex technical concepts clearly to technical, business, and executive audiences.
  • Strong stakeholder leadership skills and a proven track record of driving alignment across Product, Engineering, Research, and Business teams.
  • Experience influencing technical strategy, product direction, and organizational decision-making.

Responsibilities

  • Lead the design, development, evaluation, and deployment of production-grade AI and Machine Learning solutions.
  • Drive innovation in Generative AI, Large Language Models, Agentic AI, Retrieval-Augmented Generation, Deep Learning, and Transformer-based architectures.
  • Define the technical vision for AI products, ensuring solutions are scalable, secure, maintainable, and aligned with business objectives.
  • Provide expertise in model development, experimentation, optimization, evaluation, and production deployment.
  • Establish standard methodologies for LLM evaluation, model benchmarking, AI quality measurement, and performance assessment.
  • Evaluate emerging AI technologies, foundation models, and third-party solutions to identify opportunities for innovation and business value.
  • Guide architectural decisions across AI platforms, model-serving infrastructure, data pipelines, and MLOps/LLMOps capabilities.
  • Partner with Engineering teams to productionize AI solutions and drive operational excellence.
  • Build, mentor, and lead high-performing teams of Data Scientists and AI/ML practitioners.
  • Set clear goals, drive accountability, and support career growth and development.
  • Lead performance management, coaching, feedback, and talent development activities.
  • Foster a culture of innovation, collaboration, ownership, and continuous learning.
  • Drive hiring, onboarding, succession planning, and team growth initiatives.
  • Accelerate technical excellence through mentoring, technical reviews, and knowledge sharing.
  • Partner with Product, Engineering, and Business leaders to define AI strategy, roadmap, and priorities.
  • Drive execution through planning, prioritization, resource management, and delivery oversight.
  • Deliver high-quality AI solutions that create measurable business value.
  • Champion engineering excellence through guidelines, coding standards, experimentation, and governance.
  • Communicate technical strategy, risks, and recommendations to technical and executive collaborators.
  • Promote responsible AI, model governance, compliance, and operational risk management.

Desired Qualifications

  • A master's degree or equivalent experience, or a PhD.
  • Experience leading teams building AI products for financial services, capital markets, research, analytics, or other data-intensive domains.
  • Experience developing multi-agent systems, autonomous workflows, copilots, and intelligent AI assistants.
  • Knowledge of reinforcement learning, fine-tuning, synthetic data generation, model compression, and advanced optimization techniques.
  • Experience with Knowledge Graphs, vector databases, semantic search, retrieval systems, and Graph RAG architectures.
  • A strong understanding of Responsible AI, model governance, AI safety, privacy, regulatory compliance, and risk management frameworks.
  • Experience with DevOps, CI/CD, Kubernetes, Infrastructure as Code, containerization, and distributed computing platforms.
  • Contributions to the AI community through publications, patents, conference presentations, research, or open-source projects.

About the Company

LSEG

Global financial market infrastructure and data

LSEG provides global financial market infrastructure and data across the full value chain. It delivers data, analytics, and risk-management tools, benchmarks via FTSE Russell, regulatory and financial crime risk services, and trading, clearing, and settlement through its Capital Markets and Post Trade divisions, typically accessed via subscriptions, licensing, and fees. The company differentiates itself by offering an integrated, end-to-end platform that covers pre-trade data and analytics to post-trade processing with a broad, global footprint. Its goal is to grow over the long term by leveraging its diversified portfolio and international reach to create value for shareholders and customers.

Company size
10,001+
Company stage
IPO
Headquarters
London, United Kingdom
Founded
1801

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