At a Glance
- Tasks: Lead the AI and data strategy, delivering innovative solutions in a regulated environment.
- Company: Join StarCompliance, a leader in employee compliance technology for global financial institutions.
- Benefits: Competitive salary, growth opportunities, and a chance to shape the future of AI.
- Other info: Collaborative culture with a focus on integrity and ethical practices.
- Why this job: Make a real impact by driving AI adoption and building a high-performing team.
- Qualifications: Proven experience in AI systems, strong leadership skills, and a background in financial services.
The predicted salary is between 90000 - 110000 £ per year.
Star Compliance is a market-leading provider of employee compliance technology, trusted by 350+ global financial institutions and highly regulated organisations to manage regulatory obligations, monitor employee activity, and mitigate risk.
Backed by private equity, we are investing in the next generation of our cloud-native, AI-enabled Saa S platform — and this role will shape the AI and data future of that platform.
Role
We need a hands-on technical leader to own our data and AI strategy and execute it building a high-performing Data & AI function that ships production-grade AI capabilities at scale in a heavily regulated environment.
You will partner closely with Product, Engineering, Architecture, Security, and the executive team.
You will work together with the Product Director, AI & Data Products as a joint technology-and-product leadership pairing, co-owning the AI roadmap, prioritisation, and delivery outcomes.
Responsibilities
- AI Product Delivery & Strategy
- Own the end-to-end technical strategy and execution roadmap for AI-enabled product capabilities across the Star Compliance platform.
- Drive adoption of generative AI, LLM-based architectures, predictive analytics, graph intelligence, recommendation systems, and semantic search where these deliver measurable customer value.
- 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.
Skills and Experience
- 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).
- 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.
- 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
- All Star Compliance employees are expected to commit to a high standard of personal integrity and carry out their responsibilities in an ethical manner.
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Head Of Data Science and AI in York employer: StarCompliance
StarCompliance is an exceptional employer that prioritises employee growth and collaboration in a dynamic work environment. With a strong focus on innovation and reliability, we offer our Site Reliability Engineers the opportunity to work with cutting-edge technologies while fostering a culture of inclusivity and support. Our UK-based team enjoys flexible working arrangements, comprehensive benefits, and a commitment to professional development, making it a rewarding place to advance your career in the tech industry.
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We think you need these skills to ace Head Of Data Science and AI in York
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at StarCompliance. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
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✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
