Head of Data Science and AI UK Based

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.

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.