Principal Applied Data Scientist

Join LSEG, a leading global financial markets infrastructure and data provider. As a Principal Applied Data Scientist, you will design, build, and deploy scalable AI solutions for financial markets. You will work closely with collaborative teams, lead the end-to-end development of AI solutions, and continuously improve model quality. This is a hands-on technical leadership role with a focus on delivering production-ready AI systems.

Responsibilities

• Lead the end-to-end development of AI solutions, including design, building, testing, and deployment of models.
• Apply NLP and LLM techniques to solve real-world problems, ensuring models are optimized for performance and reliability.
• Collaborate with engineering and business partners to define requirements, shape roadmaps, and ensure successful delivery of AI products.
• Strong Python programming skills, including object-oriented design and proficiency with key ML libraries (e.g., PyTorch, TensorFlow, Scikit-Learn)

Requirements

• Solid understanding of probability and statistical modeling to support robust model development and interpretation
• Proven ability to set and uphold coding and model evaluation standards for production environments
• Excellent communication skills to articulate technical decisions and trade-offs to both technical and non-technical audiences
• Extensive hands-on experience applying NLP and LLMs to real-world problems, with a consistent track record of shipping models to production and supporting them post-deployment
• Experience with cloud platforms (especially Azure and/or AWS) and modern deployment practices for scalable AI delivery
• EDUCATION Bachelor's degree in Engineering, Computer Science, Data Science, Statistics, Mathematics, Physics or a related field, or equivalent practical experience
• Experience with TypeScript, particularly for building or contributing to production services and APIs alongside ML systems
• Familiarity with DevOps practices including CI/CD, version control, automated testing, and monitoring to support reliable model deployment and maintenance