Data Science Lead

• Design, evolve, and oversee AI and machine learning architecture supporting automated compliance validation workflows.
• Design and optimize LLM-powered retrieval and validation pipelines, including Retrieval Augmented Generation (RAG) architectures.
• Establish evaluation frameworks, benchmarking approaches, and continuous improvement processes to measure and enhance AI system performance.
• Develop explainability, traceability, and validation mechanisms that support regulatory and compliance requirements.
• Collaborate closely with ML Engineers and Backend Engineers to productionize AI components and integrate them into reliable enterprise systems.
• Drive technical decisions around embeddings, vector databases, retrieval strategies, and related AI infrastructure.
• Ensure AI workflows are reproducible, testable, maintainable, and aligned with high-quality engineering standards.
• Support the scaling of AI capabilities from individual workflows into a robust, enterprise-grade platform.
• Lead technical discussions and align architectural decisions across product, engineering, data science, and compliance stakeholders.
• Identify opportunities to improve system reliability, model performance, scalability, and operational effectiveness.

Requirements

• Strong hands-on experience in Data Science, applied Machine Learning, and production AI systems.
• Proven experience designing and deploying LLM-based solutions in production environments.
• Practical experience with Retrieval Augmented Generation architectures and LLM-driven retrieval workflows.
• Experience working with vector databases, embeddings, and embedding-generation pipelines.
• Strong Python programming skills and familiarity with modern machine learning and AI frameworks.
• Demonstrated experience designing model evaluation, benchmarking, and validation frameworks.
• Understanding of production-grade AI architecture, with an ability to move beyond proof-of-concept implementations.
• Experience operating in regulated, compliance-heavy, or otherwise highly governed environments.
• Strong ownership mindset, architectural judgment, and confidence making and influencing technical decisions.
• Excellent communication and collaboration skills, with the ability to work effectively across technical and non-technical teams.
• Experience with pharmaceutical, healthcare, or other regulated industries is a strong advantage.
• Familiarity with explainable AI methodologies, document intelligence, or NLP-heavy pipelines is beneficial.
• Experience building enterprise-scale AI platforms is highly desirable.

Benefits

• Competitive salary package.
• Opportunity to work on international, high-impact AI initiatives within a regulated enterprise environment.
• Comprehensive healthcare coverage.
• Long-term B2B contract with a stable project pipeline.
• Fully remote working model.
• Opportunity to work with modern LLM, RAG, machine learning, and AI platform technologies.
• Significant technical ownership and influence over enterprise AI architecture and delivery.