StarCompliance - Head of Data Science and AI UK Based
Requirements
• AI & Machine Learning • Proven delivery of production-grade AI and ML systems at scale, not just experimentation. • Deep experience with generative AI and LLM-based application architectures (fine-tuning, prompt engineering, RAG, agentic frameworks). • Strong knowledge of vector databases and semantic search (e.g. Pinecone, Weaviate, pgvector, Azure AI Search). • Data Engineering & Platforms • Deep background in modern data engineering, ELT/ETL patterns, and large-scale data pipeline architectures. • Hands-on experience with Snowflake (or equivalent cloud data warehouse) and associated data modelling patterns. • Strong Azure ecosystem experience: Azure Data Factory, Azure Synapse, Azure OpenAI Service, and Azure Machine Learning. • Software Engineering & Architecture • Strong software engineering fundamentals and the credibility to engage at technical depth with senior engineers. • Experience with cloud-native, microservices, and event-driven architectures on Azure (or equivalent hyperscaler). • Ability to make sound build vs buy vs integrate decisions across the AI and data tooling landscape. • Leadership & Business • Proven experience building and leading high-performing Data / AI engineering teams. • Experience within financial services, regtech, compliance, surveillance, or similarly regulated domains. • Strong commercial instincts — the ability to connect technical investment to customer value and business outcomes. • Integrity and Ethics
Responsibilities
• AI Product Delivery & Strategy • Own the end-to-end technical strategy and execution roadmap for AI-enabled product capabilities across the StarCompliance platform. • Drive adoption of generative AI, LLM-based architectures, predictive analytics, graph intelligence, recommendation systems, and semantic search where these deliver measurable customer value. • Data Platform & Engineering • Own the data foundation strategy, ensuring clean, trusted, well-governed data underpins every AI initiative. • Build robust MLOps capabilities, including model training pipelines, versioning, monitoring, A/B experimentation, and drift detection. • Internal AI Enablement • Champion pragmatic AI adoption within engineering and product development, accelerating how we build, not just what we build. • Leadership & Team Building • Build, mentor, and scale a high-performing Data & AI organisation across data engineering, data science, ML engineering and analytics. • Cross-Functional & Executive Collaboration • Work closely with the CTO and executive team to align AI and data initiatives with company strategy and commercial priorities. • Partner with the Product Director, AI & Data Products, co-owning the AI roadmap, prioritisation, and delivery outcomes. • We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Apply in one click
Upload My Resume
Drop here or click to browse · Tap to choose · PDF, DOCX, DOC, RTF, TXT