Principal Data Scientist, London Stock Exchange

• 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
• Strong Python programming skills, including object-oriented design and proficiency with key ML libraries (e.g., PyTorch, TensorFlow, Scikit-Learn)
• Solid understanding of probability and statistical modeling to support robust model development and interpretation
• Experience with cloud platforms (especially Azure and/or AWS) and modern deployment practices for scalable AI delivery
• 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
• Bachelor’s degree in Engineering, Computer Science, Data Science, Statistics, Mathematics, Physics or a related field, or equivalent practical experience

Desirable

• As a Principal Data Scientist (individual contributor), you will play a hands-on role in designing, building, and deploying scalable AI solutions that go beyond prototypes and are actively used in production
• You’ll work closely with collaborative teams to co-develop innovative products for financial markets and professionals
• We’re looking for someone who combines deep technical expertise in NLP and LLMs, strong Python engineering skills, and real-world experience shipping and supporting models in production
• You should be comfortable owning delivery timelines and ensuring that solutions meet business needs and production-grade standards
• This is a hands-on technical leadership role in a high-impact environment focused on delivering production-ready AI systems
• Lead the end-to-end development of AI solutions: design, build, test, and deploy models that are robust, scalable, and used by real users
• Apply NLP and LLM techniques to solve real-world problems, ensuring models are optimized for performance and reliability
• Continuously improve model quality through tuning, evaluation, and feedback from production usage
• Evaluate third-party AI solutions with a critical eye on performance, scalability, and integration into production environments
• Write and maintain production-grade Python code, adhering to best practices in software engineering and model development
• Collaborate with engineering and business partners to define requirements, shape roadmaps, and ensure successful delivery of AI products