Senior Product Owner, Incard

• This role is ideal for someone with a data science or analytics background who wants to turn models, metrics, and signals into clear, actionable product experiences for business users
• 4–7+ years of experience in Data Product, Analytics Product, or Data Science–led product roles
• Strong background in data science, analytics, or applied machine learning
• Experience turning data and models into real product features
• Strong understanding of metrics, experimentation, forecasting, and data pipelines
• Ability to write clear, structured specs for data and AI-driven systems
• Comfortable working with ambiguity, probabilistic outcomes, and imperfect data
• Highly organised, analytical, and outcome-focused
• Strong communication skills — able to explain complex concepts simply
• Mini-CEO mentality — you own intelligence outcomes, not just models
• Data-first thinking with strong product judgement
• Obsession with clarity, trust, and real-world usefulness
• Comfortable making decisions with incomplete or noisy data
• Low-ego, highly collaborative partner to engineers and data scientists
• Startup DNA — fast iteration, ownership, no bureaucracy

Desirable

• We’re looking for a Senior Product Owner (Data / AI) who operates like a mini-CEO for our intelligence layer
• You’ll own data-driven product domains such as analytics, forecasting, benchmarking, anomaly detection, recommendations, and AI-powered financial insights. You’ll work at the intersection of data science, analytics, AI, and product, partnering closely with Analytics, AI, Backend, and Product teams
• Act as the founder of your data/AI domain
• Own vision, scope, quality, and outcomes with minimal supervision
• Translate complex data capabilities into real product value
• Define product use cases for analytics, forecasting, and AI-driven insights
• Partner with data scientists to shape models, assumptions, and outputs
• Decide what to build vs what to infer, predict, or automate
• Define success metrics focused on accuracy, usefulness, and adoption
• Collaborate closely with Analytics and AI teams on data pipelines, models, and features
• Turn raw data, signals, and predictions into user-facing insights and workflows
• Ensure explainability, trust, and clarity in AI-powered outputs
• Balance precision with usability — perfect models that users don’t understand are not success
• Write clear product requirements, specs, and acceptance criteria for data & AI features
• Break down complex initiatives into deliverable milestones
• Support engineers and data scientists with fast decisions and prioritisation
• Manage iterations after launch based on usage, feedback, and performance
• Own validation of data quality, assumptions, and edge cases
• Ensure robustness around missing data, anomalies, and model failure modes
• Work closely with compliance and risk teams on AI governance and explainability
• Ensure responsible use of AI in regulated financial contexts
• Collaborate with frontend, mobile, backend, and customer-facing teams
• Align data products with business, regulatory, and operational needs
• Create clear internal documentation for models, metrics, and decision logic
• Support hiring and help shape your future data/AI squad