Lead Data Scientist

The Emerging Tech Standard Delivery Team drives the adoption of advanced technologies and develops enterprise‑ready solutions for D&A Operations. In this role, the individual partners with Operations and Technology teams to design data‑driven solutions that deliver clear business and customer value.  The position requires strong expertise in data analytics, NLP, deep learning, and data communication, along with the ability to learn financial content and core D&A business processes. The individual must stay current with emerging technologies—including Generative AI, Multimodal AI, LLMOps practices, AI Agents, and synthetic data techniques—and collaborate closely with operations groups and specialized machine‑learning teams to deliver scalable, innovative, and high‑impact solutions.  Role, Responsibilities & Key Accountabilities:   Strategic & Technical Skill   Hands‑on technical expertise across end‑to‑end data science initiatives, ensuring high‑quality design, development, and delivery.  Shape and refine the product vision for advanced data management and analytics frameworks spanning data acquisition, transformation, quality, and workflow automation.  Define optimal user experiences for financial analytics pipelines, integrating diverse tools, datasets, and services into cohesive workflows.  Own and drive large projects from a data science perspective, removing obstacles and taking ownership to find creative solutions.  Serve as a technical authority upholding best practice, and architectural recommendations.  Business & Stakeholder Engagement   Partner with domain experts and senior business stakeholders to identify high‑value problems and co‑create AI/ML and platform strategies.  Translate business requirements into technical specifications, solution designs, and measurable success criteria.  Communicate complex insights, findings, and solution outcomes to product, engineering, sales, proposition, support, and leadership teams.  Influence cross‑functional teams by providing clear, data‑driven recommendations and technical direction.  Advanced AI/ML Delivery & Emerging Technologies   Design, build, and optimize production‑grade AI models—including deep learning, NLP, large language models, and Retrieval‑Augmented Generation (RAG).  Demonstrate strong expertise with LLMOps and advanced MLOps frameworks, including vector databases, orchestration tools (e.g., LangChain, LlamaIndex), and scalable model‑serving platforms to manage end‑to‑end LLM lifecycle  Demonstrate strong expertise Generative AI and Multimodal AI advancements, including models that handle text, images, audio, and video in unified architectures, significantly reducing pipeline complexity  Assess third‑party AI technologies, frameworks, and tools to inform build‑versus‑buy decisions and strengthen platform capabilities.  Establish and uphold high coding standards, reproducibility practices, and quality controls for robust ML development.  Apply advanced model evaluation, tuning, scaling, and continuous improvement cycles.  Apply AI Agents and human‑AI collaboration frameworks, adopting AI as a productivity amplifier across business functions  Understanding Synthetic Data generation techniques to overcome real‑data scarcity, enhance model robustness, and support privacy‑preserving AI development  Data Engineering & Processing Expertise   Apply strong expertise in data extraction, including web scraping, crawling, entity recognition, and advanced pre/post‑processing.  Work with complex structured, semi‑structured, and unstructured datasets—including financial documents, PDFs, and scanned content.  Collaborate with data engineering teams to ensure scalable, reliable pipelines that support high‑impact analytics workflows.  Cloud, MLOps & Deployment Excellence   Align with modern MLOps workflows, CI/CD pipelines, and cloud‑native deployment practices.  Lead scalable deployment of ML/AI solutions on AWS, Azure, or equivalent cloud environments.  Partner with platform engineering to enhance monitoring, observability, and full model lifecycle management.  Continuous Improvement & Innovation   Stay abreast of emerging trends in AI, NLP, cloud computing, financial analytics, and ML engineering.  Champion experimentation, innovation, and adoption of frontier techniques and tools.  Identify opportunities to mature frameworks, modelling practices, and engineering processes across the organization.  Required Skills   Bring 8–12 years of experience in data science/AI with strong ownership of complex initiatives  Own end-to-end AI/ML lifecycle: problem definition, design, experimentation, deployment, and continuous improvement  Drive technical strategy: select models, define metrics, and balance trade-offs (performance, scalability, cost)  Lead development of advanced AI solutions: LLMs, GenAI, multimodal systems, and RAG workflows  Ensure production-grade delivery: partner with Engineering for secure, scalable, and reliable deployments (MLOps/LLMOps,CI/CD)  Define and sign off success criteria: evaluation frameworks, quality thresholds, and production readiness  Ensure Responsible AI & compliance: governance, explainability, documentation, and audit readiness  Act as technical authority: resolve risks, guide and mentor data scientists and ML engineers  Translate business needs into scalable AI solutions and influence cross-functional stakeholders  Demonstrate deep hands-on expertise in Python, ML/DL frameworks, NLP, LLMs, and RAG  Strong experience in MLOps/LLMOps, production deployment, and Git-based CI/CD workflows  Solid foundation in statistics, data engineering, and large-scale data processing  Proficiency in cloud platforms (Azure/AWS) and modern data ecosystems  Experience with AI agents, human-in-the-loop systems, and synthetic data techniques  Strong problem-solving, strategic thinking, and communication skills  Preferred   Experience in investment banking or financial services.  Experience contributing to enterprise AI governance, risk frameworks, or regulatory compliance programs.  Research publications, patents, or conference presentations in AI/ML/NLP.  Experience operating in multi‑team, matrix, or global environments.  Recognition as a technical expert or thought leader in AI/ML.  Education   Master’s degree in Statistics, Mathematics, Computer Science, or an Engineering degree specializing in Data Science/AI.  Proficiency in Python, R, and SQL.  Career Stage: Manager London Stock Exchange Group (LSEG) Information: 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. LSEG is a leading global financial markets infrastructure and data provider. Our purpose is driving financial stability, empowering economies and enabling customers to create sustainable growth. Our purpose is the foundation on which our culture is built. Our values of Integrity, Partnership , Excellence and Change underpin our purpose and set the standard for everything we do, every day. They go to the heart of who we are and guide our decision making and everyday actions. Working with us means that you will be part of a dynamic organisation of 25,000 people across 65 countries. However, we will value your individuality and enable you to bring your true self to work so you can help enrich our diverse workforce. We are proud to be an equal opportunities employer. This means that we do not discriminate on the basis of anyone’s race, religion, colour, national origin, gender, sexual orientation, gender identity, gender expression, age, marital status, veteran status, pregnancy or disability, or any other basis protected under applicable law. Conforming with applicable law, we can reasonably accommodate applicants' and employees' religious practices and beliefs, as well as mental health or physical disability