Head of Data Science

Job Description

Key Responsibilities

• Identify and prioritise methods to optimise internal processes — including migration workflows, schema mapping, QA, and project delivery
• Build and deploy tech-enabled tooling to improve delivery accuracy and efficiency
• Establish a culture of data-driven decision-making internally — supporting leadership with analytics, reporting, and operational insight
• Develop and commercialise new capabilities within the data intelligence space
• Work with clients and clinical stakeholders to understand unmet needs and translate them into AI/ML solutions
• Design, train, and productise machine learning models for healthcare use cases
• Build and maintain robust MLOps practices: versioning, monitoring, retraining pipelines, and deployment infrastructure
• Own and drive Stalis’s company-wide data science strategy — defining principles, priorities, and governance for adoption across the business
• Serve as the internal authority on technology developments — advising the leadership team on emerging technologies, competitive trends, and strategic risks and opportunities
• Define the data science team structure needed to deliver the roadmap
• Establish engineering standards, tooling choices, and ways of working for the data science function
  

Requirements

Essential

• Hands-on experience in applied machine learning and data science, ideally within healthcare or a similarly regulated sector
• Strong Python development skills and experience with ML frameworks (e.g. scikit-learn, PyTorch, HuggingFace Transformers)
• Demonstrated ability to take AI/ML projects from idea through to deployed, production-quality systems
• Experience working with databases and structured data at scale — including SQL, schema design, and ETL/data engineering concepts
• Comfortable operating as both a technical individual contributor and a strategic thinker — able to write code and present to leadership
• Strong communication skills: able to explain complex technical concepts to non-technical audiences

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