Lead Applied Data Scientist: NLP & Production AI

London Stock Exchange; Greater London; On-site; GBP 120,000 - 180,000; Full time

Benefits offered by this job

Competitive compensation

Comprehensive benefits

Career growth investment

Job summary

London Stock Exchange is seeking a Principal Data Scientist to design, build, and deploy scalable AI solutions for production use in financial markets. You will lead hands-on technical work, applying NLP/LLMs, and collaborate with cross-functional teams to deliver robust models at scale.

The role requires deep NLP/LLM expertise, Python engineering skills, and experience shipping models in production with cloud deployment practices (Azure/AWS).

Qualifications

• Hands-on experience shipping models to production and supporting them post-deployment.
• Strong Python programming skills and object-oriented design.
• Solid understanding of probability and statistical modeling.
• Experience applying NLP and LLMs to real-world problems.

Responsibilities

• Lead end-to-end development of AI solutions: design, build, test, and deploy models.
• Write production-grade Python code following software engineering best practices.
• Collaborate with engineering and business partners to define requirements and roadmaps.
• Evaluate third-party AI solutions for performance, scalability, and production integration.
• Own delivery timelines and ensure solutions meet business needs and production standards.

Skills

Python programming

NLP & LLMs

ML libraries (PyTorch, TensorFlow, Sci

Education

Bachelor's degree (Engineering, CS, Data Science, Stats, Maths, Physics)

Tools

TypeScript

Cloud platforms (Azure/AWS)

Job description

London Stock Exchange is seeking a Principal Data Scientist to design, build, and deploy scalable AI solutions for production use in financial markets. You will lead hands-on technical work, applying NLP/LLMs, and collaborate with cross-functional teams to deliver robust models at scale.

The role requires deep NLP/LLM expertise, Python engineering skills, and experience shipping models in production with cloud deployment practices (Azure/AWS).