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This is a remote position.
We are looking for a Data Science Lead to join a high-impact AI programme operating within a strictly regulated pharmaceutical environment. The role focuses on leading the machine learning and GenAI architecture behind a production-grade compliance platform that automates regulatory validation of marketing materials. This is a strategic, enterprise-scale initiative requiring strong ownership, architectural thinking, and hands-on depth in LLM-driven systems. Responsibilities
Design and evolve AI and ML architecture supporting compliance validation workflows
Design and optimise LLM-driven retrieval and validation pipelines (RAG-based systems)
Own evaluation frameworks, benchmarking strategies, and continuous improvement loops
Implement explainability and traceability mechanisms aligned with regulatory standards
Collaborate closely with ML Engineers and Backend teams on productionisation of AI components
Drive decisions around embeddings, vector databases, and retrieval strategies
Ensure reproducible, testable, and high-quality AI workflows
Support scaling the platform into an enterprise-grade AI solution
Lead technical discussions across product, engineering, and compliance stakeholders
Requirements
Strong hands-on background in Data Science and applied Machine Learning
Proven experience designing and deploying LLM-based systems in production
Practical experience with Retrieval Augmented Generation architectures
Experience with vector databases and embedding pipelines
Strong Python expertise and familiarity with modern AI frameworks
Experience designing model evaluation and validation frameworks
Ability to operate in regulated or compliance-heavy environments
Strong ownership mindset and ability to influence architectural decisions
Confident communication skills in cross-functional environments
Nice to have
Experience in pharmaceutical, healthcare, or other regulated industries
Exposure to explainable AI methodologies or frameworks
Experience with document intelligence and NLP-heavy pipelines
Background in enterprise-scale AI platforms rather than proof-of-concept environments
Benefits
Solid, competitive salary
Work in a multinational environment on international projects
Comprehensive healthcare
Long-term B2B contract with a stable project pipeline
