Data Scientist

Overview

In this role you will develop and deploy enterprise-scale AI and machine learning solutions to boost operational efficiency, resilience, and customer outcomes. You will collaborate with engineers, product teams, and stakeholders to turn data into measurable business value and guide AI products from concept to production. You will assess emerging AI technologies and implement practical applications across the organisation. This is an opportunity to shape data-driven decision making in a globally diverse, innovative environment.

Pay / Benefits

• Healthcare
• Retirement planning
• Paid volunteering days
• Wellbeing initiatives

Responsibilities

• Design, build and deploy machine learning models to address operational and business challenges
• Develop predictive analytics for incident prevention, service health, risk and customer experience
• Support the development of Generative AI applications using LLMs, retrieval systems and agentic workflows
• Evaluate emerging AI technologies and recommend practical organisational applications
• Perform advanced statistical analysis on large-scale datasets and develop feature engineering and evaluation frameworks
• Create 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 measurement frameworks for AI products
• Support model monitoring, governance and continuous improvement activities
• Contribute to AI product roadmaps and prioritisation discussions
• Collaborate with SMEs across Engineering, Operations, Service Management and Product
• Communicate complex concepts to technical and non-technical audiences
• Contribute to growing LSEG’s AI/Data Science community through knowledge sharing and mentoring
• Promote responsible AI and data-driven decision making

Key requirements

• Demonstrable experience delivering ML/AI solutions in a large-scale enterprise
• Strong background in statistical modelling, predictive analytics and experimentation
• Experience with structured and unstructured data at scale
• Ability to translate business problems into measurable analytical outcomes
• Experience taking models from development to production environments
• Python and data-science ecosystem (Pandas, NumPy, Scikit-learn)
• ML techniques including classification, regression, clustering and anomaly detection
• Experience with 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 visualization and storytelling tools

• Strong problem-solving and analytical thinking
• Curiosity and passion for applying AI to real-world challenges
• Customer-focused mindset with outcome orientation
• Python ecosystem (Pandas, NumPy, Scikit-learn)
• ML techniques: classification, regression, clustering, anomaly detection
• Generative AI, LLMs, RAG architectures

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