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. You need strong expertise in data analytics, NLP, deep learning and data communication, and the ability to learn financial content and core D&A 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 partner with analytics experts to turn business needs into a practical platform strategy.
• Lead the strategy's execution with engineering and project delivery teams, and communicate progress to product, engineering, business, sales and support leaders.
• Develop and deploy production-grade AI models, tune them for accuracy, performance and scalability, and evaluate third-party AI capabilities.
• Establish coding standards, work with software engineers on requirements and roadmaps, and build evaluation frameworks for automation solutions.
• Build solutions and onboarding workloads on AWS or other cloud platforms, and use web crawling, entity extraction and document processing (PDFs and scanned documents).

Required profile

• 6-8 years of experience in data science, analytics or statistical modelling roles.
• Hands-on experience in NLP, LLMs, prompt engineering and RAG, and in training, testing and validating deep learning and classical machine learning models.
• Experience building solutions and onboarding workloads on AWS or other cloud platforms.
• Excellent communication and presentation skills, able to explain complex technical topics to technical and business audiences.

Required skills

• Python and core ML/DL libraries (TensorFlow, PyTorch, Scikit-learn)
• Statistical analysis, probability and applied statistics
• Git-based version control and CI/CD pipelines (GitLab/GitHub runners)
• Relational databases, large-scale data processing and statistical programming
• MLOps principles and deployment on AWS and/or Azure
• Commercial or open-source data management tools