Business Analyst

Overview

In this role you own the AI value creation across the Commcise product suite, shaping a clear AI roadmap and driving delivery with engineering. You will lead AI adoption in both product and development processes, prioritising high-impact opportunities and validating ideas with clients. A key focus is improving testing and QA through AI-driven approaches to accelerate delivery and quality. You join a fast-moving fintech with a global client base and help translate what AI can realistically achieve into measurable business impact.

Responsibilities

• Own the AI product vision, strategy and roadmap for the Commcise suite, aligned to commercial and client priorities
• Identify and prioritise high-value AI opportunities; decide what gets built, and what does not
• Define clear requirements, success metrics and acceptance criteria for engineering delivery
• Champion AI adoption in how we build and test software, focusing on reducing testing drag
• Engage clients and internal stakeholders to validate direction and track adoption and impact post-launch
• Present persuasively to technical and non-technical audiences, building consensus with evidence
• Stay current with the AI landscape and bring pragmatic opportunities into the business
• Balance ambition with feasibility and work with engineering on cost, risk, effort and value

Key requirements

• Approximately 5–8 years in product management or a closely related role with a track record of shipping products
• Demonstrable experience defining and delivering AI-powered product features (LLMs, RAG, model APIs)
• Strong product instincts: prioritisation, requirement definition and outcome measurement
• Sufficient technical fluency to collaborate with engineers and make sound trade-off decisions
• Excellent communication and presentation skills to influence and persuade
• Genuine commitment to driving AI adoption across product and delivery

• influence and consensus-building
• commercial-mindedness
• collaboration across product, engineering and business teams
• AI/ML concepts (LLMs, RAG, model APIs)
• API-driven product delivery
• AI-driven testing and QA approaches (test generation, coverage, automation)

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