Data Science Lead

Accountabilities

• • 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.