Data Science Manager

London Stock Exchange; Greater London; On-site; GBP 120,000 - 180,000; Full time

Job summary

London Stock Exchange is seeking an experienced Data Science Manager to lead a team of Data Scientists and ML Engineers, driving AI product development and production-grade solutions for financial markets.

You will partner with Product, Engineering, Research, and business partners to shape strategy, accelerate execution, and deliver measurable value at scale. Leadership, mentoring, and a growth-oriented culture are essential.

Qualifications

• Experience leading technical teams delivering production-grade AI/ML solutions.
• Strong track record in building AI products for data-intensive domains.
• Proven ability to align AI strategy with business goals and stakeholder needs.

Responsibilities

• Lead a high-performing team of Data Scientists and ML Engineers.
• Define and execute the AI strategy, roadmaps, and priorities.
• Partner with Product, Engineering, Research, and business teams to deliver AI solutions.

Skills

Leadership

Strategy

People leadership

Communication

Cloud computing

Model evaluation

Regulatory compliance

Education

Master’s or PhD in CS/DS/Math

Tools

Python

PyTorch

TensorFlow

MLOps

Kubernetes

Azure ML

LangChain

RAG

Job description

• Lead a team of exceptional Data Scientists and ML Engineers building next-generation AI solutions for financial markets. Drive innovation using LLMs, Generative AI, Deep Learning, Transformers, Agentic AI, and advanced Machine Learning to deliver real-world business impact
• As a Data Science Manager, you will lead a high-performing team of Data Scientists delivering AI-powered products that create measurable business value at scale
• This role combines technical leadership, people leadership, and strategic execution. You will drive innovation in AI and Machine Learning, establish engineering excellence, and develop exceptional talent while delivering production-grade solutions that solve complex customer problems
• You will partner closely with Product, Engineering, Research, and business partners to shape strategy, accelerate execution, and deliver impactful AI solutions
• Lead the design, development, evaluation, and deployment of production-grade AI and Machine Learning solutions
• Drive innovation in Generative AI, Large Language Models (LLMs), Agentic AI, Retrieval-Augmented Generation (RAG), 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 deep 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 find opportunities for innovation and business value
• Guide architectural decisions across AI platforms, model-serving infrastructure, data pipelines, and MLOps/LLMOps capabilities
• Partner closely 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 effective 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 clearly to technical and executive collaborators
• Promote responsible AI, model governance, compliance, and operational risk management
• Lead the development of next-generation AI products, demonstrating large-scale datasets, advanced AI models, and modern technologies to solve complex customer challenges
• Shape AI strategy, influence product direction, and build capabilities that deliver dynamic outcomes for customers worldwide
• Work at the forefront of Generative AI, LLMs, Agentic AI, Analytics, and Intelligent Search, helping define the future of AI innovation in financial markets
• Accelerate your leadership and technical career through continuous learning, innovation, and exposure to large-scale AI initiatives

Are you an experienced Data Science leader with a passion for building and scaling AI/ML products?The ideal candidate has a consistent track record of leading technical teams, scaling AI initiatives from concept to production, and fostering a culture of innovation, collaboration, and continuous improvementStrong coaching, mentoring, performance management, and talent development capabilitiesProven track record of building, leading, and developing high-performing teams of Data Scientists, ML Engineers, and AI practitionersDemonstrated success delivering large-scale AI initiatives from ideation to production with measurable business impactExceptional leadership, communication, and influencing skills with experience engaging senior leadership and executive collaboratorsExperience leading multiple concurrent programs, balancing priorities, resources, and customer expectationsAdvanced proficiency in Python and modern AI frameworks, including PyTorch, TensorFlow, Scikit-Learn, Hugging Face, LangChain, Semantic Kernel, and related ecosystemsExtensive experience designing, building, and deploying production-grade AI and Machine Learning solutions at scaleHands-on experience with LLM evaluation, benchmark design, model validation, prompt engineering, guardrails, and AI quality assessmentStrong understanding of model observability, monitoring, evaluation frameworks, experimentation, reliability, and operational perfectionGood foundation in statistics, probability, optimization, experimentation, and applied machine learningExpertise in cloud-native AI development using Azure AI Foundry, Azure Machine Learning, Azure OpenAI, Azure AI Search, AWS AI Services, and modern cloud architecturesExperience building and scaling enterprise AI platforms, ML infrastructure, and intelligent applications serving thousands of usersDeep expertise in LLMs, Generative AI, RAG, Agentic AI, Deep Learning, Neural Networks, and Transformer architecturesExperience implementing MLOps and LLMOps practices, including CI/CD, model lifecycle management, governance, and production operations at scaleStrong interested party leadership skills with a proven track record to drive alignment across Product, Engineering, Research, and Business teamsAbility to communicate complex technical concepts clearly to technical, business, and executive audiencesKnown to work influencing technical strategy, product direction, and organizational decision-makingExperience leading teams building AI products for financial services, capital markets, research, analytics, or other data-intensive domainsExperience developing multi-agent systems, autonomous workflows, copilots, and intelligent AI assistantsKnowledge of reinforcement learning, fine-tuning, synthetic data generation, model compression, and advanced optimization techniquesExperience with Knowledge Graphs, vector databases, semantic search, retrieval systems, and Graph RAG architecturesStrong understanding of Responsible AI, model governance, AI safety, privacy, regulatory compliance, and risk management frameworksExperience with DevOps, CI/CD, Kubernetes, Infrastructure as Code, containerization, and distributed computing platformsContributions to the AI community through publications, patents, conference presentations, research, or open-source projectsMaster’s or equivalent experience or PhD preferredBachelor’s degree or equivalent experience in Computer Science, Data Science, Statistics, Mathematics, Engineering, Physics, Artificial Intelligence, Machine Learning, or a related quantitative field