Data Science Manager
On-siteEdinburgh, Scotland, United Kingdom
Edinburgh, Scotland, United KingdomOn-siteFull TimeFinancial ServicesEnterprise
Full TimeEnterpriseFinancial Services
Job Summary
Lead a high-performing team of Data Scientists and ML Engineers delivering AI-powered products that create measurable business value at scale. Drive innovation in Generative AI, Large Language Models, and Agentic AI to design, develop, and deploy production-grade solutions for financial markets. Partner with Product, Engineering, and business leaders to define strategy, accelerate execution, and establish engineering excellence across model serving, MLOps, and data pipelines. Mentor talent, manage performance, and foster a culture of continuous learning while ensuring responsible AI and operational risk management.
Required Qualifications
- Proven track record of building, leading, and developing high-performing teams of Data Scientists, ML Engineers, and AI practitioners
- Demonstrated success delivering large-scale AI initiatives from ideation to production with measurable business impact
- Strong coaching, mentoring, performance management, and talent development capabilities
- Experience leading multiple concurrent programs, balancing priorities, resources, and customer expectations
- Exceptional leadership, communication, and influencing skills with experience engaging senior leadership and executive collaborators
- Extensive experience designing, building, and deploying production-grade AI and Machine Learning solutions at scale
- Deep expertise in LLMs, Generative AI, RAG, Agentic AI, Deep Learning, Neural Networks, and Transformer architectures
- Hands-on experience with LLM evaluation, benchmark design, model validation, prompt engineering, guardrails, and AI quality assessment
- Good foundation in statistics, probability, optimization, experimentation, and applied machine learning
- Advanced proficiency in Python and modern AI frameworks, including PyTorch, TensorFlow, Scikit-Learn, Hugging Face, LangChain, Semantic Kernel, and related ecosystems
- Experience building and scaling enterprise AI platforms, ML infrastructure, and intelligent applications serving thousands of users
- Strong understanding of model observability, monitoring, evaluation frameworks, experimentation, reliability, and operational perfection
- Expertise in cloud-native AI development using Azure AI Foundry, Azure Machine Learning, Azure OpenAI, Azure AI Search, AWS AI Services, and modern cloud architectures
- Experience implementing MLOps and LLMOps practices, including CI/CD, model lifecycle management, governance, and production operations at scale
- Ability to communicate complex technical concepts clearly to technical, business, and executive audiences
- Strong interested party leadership skills with a proven track record to drive alignment across Product, Engineering, Research, and Business teams
- Known to work influencing technical strategy, product direction, and organizational decision-making
- Bachelor's degree or equivalent experience in Computer Science, Data Science, Statistics, Mathematics, Engineering, Physics, Artificial Intelligence, Machine Learning, or a related quantitative field
Desired Qualifications
- Master's or equivalent experience or PhD preferred
- Experience leading teams building AI products for financial services, capital markets, research, analytics, or other data-intensive domains
- Experience developing multi-agent systems, autonomous workflows, copilots, and intelligent AI assistants
- Knowledge of reinforcement learning, fine-tuning, synthetic data generation, model compression, and advanced optimization techniques
- Experience with Knowledge Graphs, vector databases, semantic search, retrieval systems, and Graph RAG architectures
- Strong understanding of Responsible AI, model governance, AI safety, privacy, regulatory compliance, and risk management frameworks
- Experience with DevOps, CI/CD, Kubernetes, Infrastructure as Code, containerization, and distributed computing platforms
- Contributions to the AI community through publications, patents, conference presentations, research, or open-source projects
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