Senior Data Scientist - Financial Analytics

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. As Senior Data Scientist, you partner with Operations and Technology teams to design data-driven solutions that deliver clear business and customer value. The role requires expertise in data analytics, NLP, deep learning and data communication, and the ability to learn financial content and core business processes.

Key responsibilities

• Own the product vision for a data management framework covering data acquisition, cleaning, transformation and related workflows.
• Support the Lead Data Scientist on the end-to-end AI/ML lifecycle, from problem definition and design to deployment and continuous improvement.
• Define an optimized user experience for financial analytics data pipelines and unify multiple tools and services.
• Translate business data needs into a practical platform and framework strategy, then lead its execution with engineering and project delivery teams.
• Communicate product goals, progress and achievements to product, engineering, business, sales, support and other stakeholders.
• Develop and deploy production-grade AI models, tune them for accuracy, performance and scalability, and evaluate third-party AI capabilities.
• Set up evaluation frameworks for automation solutions and establish coding standards.
• Work with software engineers and cross-functional teams to define requirements and shape development roadmaps.

Required profile

• 6-8 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.
• Experience building solutions and onboarding workloads on AWS or other cloud platforms.
• Preferred: experience in large financial services or investment banking, familiarity with Refinitiv or LSEG products, and experience with global or distributed teams.

Required skills

• Python and core ML/DL libraries (TensorFlow, PyTorch, Scikit-learn); proficiency in Python, R and SQL.
• NLP, LLMs, prompt engineering and RAG; training, testing and validation of deep learning and classical ML models.
• Web crawling and scraping, entity identification and extraction, and processing of structured, semi-structured and unstructured data from PDFs and scanned documents.
• Git-based version control and CI/CD pipelines (GitLab/GitHub runners).
• MLOps principles and deployment on AWS and/or Azure.
• Excellent communication and presentation skills, and the ability to simplify complex technical concepts for technical and business audiences.