• 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. • 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. • Champion pragmatic AI adoption within engineering and product development, accelerating how we build, not just what we build. • Build, mentor, and scale a high-performing Data & AI organisation across data engineering, data science, ML engineering and analytics. • 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.

• This role will focus on the outward-facing integration layer—ensuring that mainframe data and services can be effectively consumed by digital channels, analytics platforms, data lakes, technologies, and modern applications. • Own the services that expose mainframe transactions and data as modern, consumable APIs for digital use cases, working closely with product and engineering teams to package and deliver mainframe services to external clients using API enablement. • Define and communicate a clear product vision for our mainframe hybrid data & integration services portfolio. • Craft compelling value propositions that resonate with both technical buyers and business stakeholders. • Drive the commercial success of your product portfolio, including pricing strategy, packaging, sales enablement, and go-to-market planning. • Own the product roadmap for your portfolio, making prioritization decisions based on customer value, strategic alignment, and commercial impact. • Develop deep understanding of customer needs, pain points, and buying behaviors related to mainframe data accessibility and integration challenges. • Work cross-functionally with engineering, architecture, sales, marketing, finance, and customer success teams.

• Design and implement advanced ML models and statistical methods to optimize forecasting, risk assessment, and decision-making processes. • Conduct data provenance tracking, ensuring documentation of sources, transformations, and lineage for compliance with governance policies. • Submit the Data Provenance & Lineage Report, summarizing transformation workflows, feature engineering processes, and audit compliance. • Implement sprint-based Agile methodologies, ensuring rapid development cycles, backlog grooming, and alignment with mission requirements. • Provide a Rough Order of Magnitude (ROM) Estimate Report before each analytics project, detailing expected Full-Time Equivalent (FTE) hours, compute costs, storage consumption, and infrastructure requirements. • Conduct quarterly reviews to track cost efficiency, assess system performance, and optimize analytic workflows through the Quarterly Cost & Resource Utilization Report.

• Develop and refine predictive models. • Conduct exploratory data analysis. • Generate AI-driven insights to enhance intelligence and operational planning. • Integrate customer feedback into model iteration cycles, leveraging Agile development methodologies. • Submit Predictive Model Performance Report, documenting key findings, model accuracy metrics, and operational impact assessments. • Implement sprint-based Agile methodologies, ensuring rapid development cycles, backlog grooming, and alignment with mission requirements. • Conduct quarterly reviews to track cost efficiency, assess system performance, and optimize analytic workflows through the Quarterly Cost & Resource Utilization Report.