Data Scientist

London Stock Exchange; Greater London; On-site; GBP 90,000 - 120,000; Full time

Job summary

London Stock Exchange is seeking an experienced Data Scientist to develop and deploy enterprise-scale AI and ML solutions that improve operational efficiency, service resilience and customer outcomes. You will work with software engineers, SREs, product managers and stakeholders to move projects from concept to production.

The role focuses on building ML models, predictive analytics, and AI applications using Generative AI and LLMs, with an emphasis on governance, observability and measurable

Qualifications

• Experience delivering ML/AI at enterprise scale.
• Strong statistical modeling and experimentation.
• Ability to translate business problems into analytics.

Responsibilities

• Design, build and deploy ML models for enterprise-scale use.
• Develop predictive analytics for service resilience and customer outcomes.
• Collaborate with engineers, product managers, and stakeholders.
• Define success metrics and production readiness criteria.

Skills

Python

SQL

ML/AI Solutions

Pandas/NumPy

LLMs/Generative AI

MLOps

Cloud Platforms

Data Visualization

Stakeholder communication

Tools

Azure AI

Jupyter

Git

MLflow

Power BI

Job description

• We are seeking 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 identify opportunities, build predictive models, evaluate AI solutions and take products from concept through to production
• Design, build and deploy machine learning models to solve operational and business challenges
• Develop predictive analytics capabilities focused on incident prevention, service health, operational risk and customer experience
• Support the development of Generative AI applications leveraging LLMs, retrieval systems and agentic workflows
• Evaluate emerging AI technologies and recommend practical applications across the organisation
• Perform advanced statistical analysis and modelling across large-scale operational and engineering datasets
• Develop feature engineering, experimentation and model evaluation frameworks
• Create insights and recommendations that drive decision making for senior stakeholders
• Build and maintain reproducible data science workflows and pipelines
• Partner with engineering and product teams to transition solutions from proof-of-concept into production
• Define success metrics and measurement frameworks for AI products
• Support model monitoring, governance and continuous improvement activities
• Contribute to AI product roadmaps and prioritisation discussions
• Work with subject matter experts from Engineering, Operations, Service Management and Product organisations
• Communicate technical concepts clearly to both technical and non-technical audiences
• Contribute to the growth of LSEG’s AI and Data Science community through knowledge sharing and mentoring
• Promote responsible AI, model governance and data-driven decision making

The ideal candidate combines strong technical expertise with commercial awareness and the ability to translate complex data into actionable insights and measurable business valueExperience working with structured and unstructured data at scaleDemonstrable experience delivering machine learning or AI solutions within a large-scale enterprise environmentProven ability to translate business problems into measurable analytical outcomesStrong experience applying statistical modelling, predictive analytics and experimentation techniquesExperience taking models from development into production environmentsSQL and large-scale data analysisData visualisation and storytelling toolsPython and associated data science ecosystem (Pandas, NumPy, Scikit-learn)Machine learning techniques including classification, regression, clustering and anomaly detectionCloud-based data science platforms and MLOps practicesExperience with Generative AI, LLMs, RAG architectures or agent-based AI solutionsOwnership and accountability for deliveryStrong problem-solving capability and analytical thinkingCustomer-focused mindset with a focus on outcomesClear communication and stakeholder management skillsCommitment to continuous learning and innovationCuriosity and a passion for applying AI to real-world challengesAbility to work effectively across organisational boundariesExperience within financial services, regulated environments or market infrastructureFamiliarity with Azure AI services and Microsoft AI ecosystemExperience developing AI-powered automation solutionsKnowledge of Responsible AI and model governance frameworksExperience with observability, SRE or operational analytics use cases