Data Scientist - Emerging Tech Standard Delivery

About the role

The Emerging Tech Standard Delivery Team at LSEG drives the adoption of advanced technologies and builds enterprise-ready solutions for D&A Operations. The Data Scientist works with Operations and Technology teams to design data-driven solutions that deliver clear business and customer value, partnering with operations groups and machine learning teams on scalable, high-impact solutions.

Key responsibilities

• Help build an enterprise-grade data management framework covering acquisition, cleaning, transformation and workflow optimisation.
• Design and refine user experiences for financial analytics data pipelines, integrating multiple tools and services.
• Work with analytics experts to turn business data requirements into platform components, models or analytical workflows.
• Collaborate with engineering and project delivery teams to implement data solutions aligned with product and organisational goals.
• Communicate project progress, insights and outcomes to product, engineering and business stakeholders.
• Stay current with emerging technologies, modelling techniques and market trends in financial analytics.
• Build, train, test and validate machine learning and deep learning models, including NLP, LLMs and Retrieval-Augmented Generation (RAG).
• Develop evaluation frameworks and metrics for automation and AI solutions.
• Apply web scraping, crawling, entity extraction and pre/post-processing to prepare structured, semi-structured and unstructured data, including PDFs and scanned documents.
• Deploy data and ML solutions on cloud platforms (AWS or similar) following MLOps and CI/CD best practices.

Required profile

• 3-5 years of experience in data science, analytics or statistical modelling roles.
• Master's degree in Statistics, Mathematics or Computer Science with a Data Science certification, or an Engineering degree specialising in Data Science and AI.
• Ability to learn financial content and core D&A business processes.
• Preferred: experience in financial services or investment banking, familiarity with Refinitiv or LSEG datasets, and experience with distributed or cross-location teams.

Required skills

• Python and data science libraries (Pandas, NumPy, Scikit-learn); familiarity with TensorFlow or PyTorch.
• NLP, LLMs and RAG workflows, feature engineering and predictive modelling.
• Git and CI/CD pipelines (GitLab/GitHub runners).
• MLOps practices and cloud environments (AWS/Azure).
• Relational databases and SQL; hands-on experience with commercial or open-source data management tools.