Data Scientist - AI & Machine Learning

About the role

LSEG (London Stock Exchange Group) is looking for an experienced Data Scientist to help develop and deploy enterprise-scale AI and machine learning solutions that improve operational efficiency, service resilience and customer outcomes. You will work closely with software engineers, SREs, product managers, platform teams and business stakeholders to build predictive models, evaluate AI solutions and take products from concept through to production.

Key responsibilities

• Design, build and deploy machine learning models for operational and business challenges, including predictive analytics for incident prevention, service health, operational risk and customer experience.
• Support Generative AI applications using LLMs, retrieval systems and agentic workflows, and evaluate emerging AI technologies.
• Perform advanced statistical analysis and modelling on large-scale operational and engineering datasets, and build feature engineering, experimentation and model evaluation frameworks.
• Create insights and recommendations for senior stakeholders, and maintain reproducible data science workflows and pipelines.
• Partner with engineering and product teams to move solutions from proof-of-concept to production, define success metrics and support model monitoring, governance and continuous improvement.
• Communicate technical concepts to technical and non-technical audiences, mentor others and promote responsible AI and model governance.

Required profile

• Demonstrable experience delivering machine learning or AI solutions in a large-scale enterprise environment.
• Strong experience with statistical modelling, predictive analytics and experimentation.
• Experience with structured and unstructured data at scale, and moving models from development into production.
• Ability to turn business problems into measurable analytical outcomes.
• Desirable: experience in financial services, regulated environments or market infrastructure, and observability, SRE or operational analytics use cases.

Required skills

• Python and the data science ecosystem (Pandas, NumPy, Scikit-learn).
• Machine learning: classification, regression, clustering and anomaly detection.
• Generative AI, LLMs, RAG architectures or agent-based AI solutions.
• SQL and large-scale data analysis; cloud-based data science platforms and MLOps practices.
• Data visualisation and storytelling tools.
• Desirable: Azure AI services and Microsoft AI ecosystem.