At a Glance
- Tasks: Lead a team to build innovative AI solutions for financial markets.
- Company: Join a dynamic global leader in financial markets infrastructure.
- Benefits: Enjoy competitive pay, healthcare, and flexible working options.
- Other info: Be part of a culture that values diversity, innovation, and continuous learning.
- Why this job: Shape the future of AI while making a real impact in finance.
- Qualifications: Proven experience in leading AI teams and delivering impactful projects.
The predicted salary is between 75600 - 92400 £ per year.
Are you an experienced Data Science leader with a passion for building and scaling AI/ML products? 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.
ROLE SUMMARY
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.
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 improvement. You will partner closely with Product, Engineering, Research, and business partners to shape strategy, accelerate execution, and deliver impactful AI solutions.
WHAT YOU'LL BE DOING
This role combines technical leadership, organizational leadership, and strategic execution within a high-impact AI organization.
- 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.
Strategic & Delivery Leadership
- 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.
WHAT YOU'LL BRING
ESSENTIAL SKILLS
- 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.
- Exceptional leadership, communication, and influencing skills with experience engaging senior leadership and executive collaborators.
TECHNICAL EXPERTISE
- 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.
- 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, Semantic Kernel, and related ecosystems.
- Experience building and scaling enterprise AI platforms, ML infrastructure, and intelligent applications serving thousands of users.
- Strong understanding of model observability, monitoring, evaluation frameworks, experimentation, reliability, and operational perfection.
- 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.
COLLABORATION & COMMUNICATION
- Ability to communicate complex technical concepts clearly to technical, business, and executive audiences.
- Strong interested party leadership skills with a proven track record to drive alignment across Product, Engineering, Research, and Business teams.
- Known to work influencing technical strategy, product direction, and organizational decision-making.
DESIRABLE SKILLS
- 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.
- 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.
EDUCATION
Bachelor's degree or equivalent experience in Computer Science, Data Science, Statistics, Mathematics, Engineering, Physics, Artificial Intelligence, Machine Learning, or a related quantitative field. Master's or equivalent experience or PhD preferred.
WHAT YOU’LL GET IN RETURN
- HIGH-IMPACT AI LEADERSHIP Lead the development of next-generation AI products, demonstrating large-scale datasets, advanced AI models, and modern technologies to solve complex customer challenges.
- SIGNIFICANT OWNERSHIP Shape AI strategy, influence product direction, and build capabilities that deliver dynamic outcomes for customers worldwide.
- INDUSTRY LEADERSHIP Work at the forefront of Generative AI, LLMs, Agentic AI, Analytics, and Intelligent Search, helping define the future of AI innovation in financial markets.
- CAREER GROWTH Accelerate your leadership and technical career through continuous learning, innovation, and exposure to large-scale AI initiatives.
We recognize that attracting exceptional talent requires flexibility and inclusivity. We take a hybrid-first approach and are committed to creating an environment where different perspectives, continuous learning, and innovation can thrive. We encourage applications from individuals of all backgrounds and experiences and are committed to providing equal opportunities for all.
Join us and be part of a team that values innovation, quality, and continuous improvement. If you're ready to take your career to the next level and make a significant impact, we'd love to hear from you.
Data Science Manager in London employer: London Stock Exchange Group
At London Stock Exchange Group, we pride ourselves on being an exceptional employer that champions innovation and sustainability. Our collaborative work culture fosters professional growth, offering employees ample opportunities to develop their skills in a dynamic environment focused on sustainable investment. Located in the heart of London, we provide a vibrant workplace with access to industry-leading resources and a commitment to making a positive impact in the financial sector.
Contact Details:
London Stock Exchange Group Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land Data Science Manager in London
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We think you need these skills to ace Data Science Manager in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Craft a Tailored Cover Letter:For a full-time role at London Stock Exchange Group, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at London Stock Exchange Group. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at London Stock Exchange Group
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at London Stock Exchange Group!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
