Manager Data Science (AI)

Location: London Wall, United Kingdom

Company

hirify.global is a global information and analytics business supporting scientific research, health education, healthcare, and clinical practice through quality information, data, and analytics.

What you will do

• Lead, coach, recruit, retain, and develop a team of data scientists while managing performance, capacity, priorities, and career development.
• Set team strategy and establish best practices for experimentation, model evaluation, data quality, production collaboration, and technical documentation.
• Guide the design and end-to-end delivery of machine learning and AI systems, from problem framing and experimentation through deployment, monitoring, and iteration.
• Lead systems combining retrieval, ranking, and generation, including LLM-powered workflows, while balancing performance, product needs, reliability, scalability, cost, and time to value.
• Define evaluation strategies covering model quality, retrieval and ranking performance, user outcomes, business impact, offline evaluation, online testing, error analysis, and human-in-the-loop processes.
• Partner with product, engineering, design, and other stakeholders to translate customer needs into measurable data science opportunities and influence data and AI strategy.

Requirements

• Extensive experience in data science, machine learning, or a related field.
• Direct experience managing, coaching, or leading data scientists or similarly technical professionals, with a track record of delivering complex, production-quality data science or AI solutions through a team.
• Deep understanding of machine learning fundamentals, experimentation, statistical evaluation, and model performance assessment.
• Strong Python skills and technical judgment for reviewing and guiding production-quality, maintainable systems.
• Experience with large, complex, and unstructured datasets, and the ability to manage multiple priorities, make sound trade-offs, and deliver.
• Strong stakeholder-management and communication skills.
• Practical experience with large language models in applied or production settings, retrieval approaches including embeddings, semantic search, and ranking, and LLM-powered systems involving multi-step reasoning, tool use, or workflow orchestration.
• Strong judgment in selecting classical machine learning, retrieval, or LLM-based approaches, plus experience designing evaluation frameworks for AI-generated or retrieval-based outputs.
• Familiarity with PyTorch, TensorFlow, Scikit-learn, Hugging Face, cloud platforms, Git, CI/CD, MLOps practices, scalable system design, and vector databases or search infrastructure such as Elasticsearch, OpenSearch, or Qdrant.

Nice to have

• Experience with search, information retrieval, recommendation, or knowledge systems at scale.
• Experience with agentic AI systems, workflow orchestration, LangChain, or LangGraph.
• Experience optimizing AI systems for performance, scalability, reliability, or cost.
• Experience developing responsible AI governance or review practices.
• Experience building teams or leading organizational change in a data or AI environment.

Culture & Benefits

• Work on impactful problems involving complex, real-world data and systems combining machine learning with modern AI approaches.
• Flexible working hours that allow adjustment of working times during the day.
• Wellbeing initiatives, shared parental leave, study assistance, and sabbaticals.
• Focus on scientific rigor, collaboration, customer focus, responsible AI, continuous improvement, and work-life balance.
• Country-specific benefits are available.