AI Product Owner

Description

We are looking for an experienced and forward-thinking AI Product Owner to join Digitain's AI Lab and drive the development of next-generation Artificial Intelligence solutions across our iGaming ecosystem. The AI Product Owner will act as the bridge between business stakeholders, data scientists, machine learning engineers, software development teams, and product leaders to transform AI opportunities into scalable products that generate measurable business value. 

This role requires a combination of product management expertise, data-driven decision making, AI/ML understanding, and strong stakeholder management skills. The successful candidate will own the AI product roadmap, prioritize initiatives, define requirements, and ensure successful delivery of AI-powered products that enhance customer experience, operational efficiency, responsible gaming, fraud prevention, CRM effectiveness, and business intelligence. 

Responsibilities 

AI Product Strategy & Roadmap 

• Define and maintain the AI Lab product roadmap aligned with company objectives and strategic priorities.  
• Identify and evaluate AI opportunities across sportsbook, casino, CRM, payments, fraud detection, risk management, customer support, and responsible gaming.  
• Translate business challenges into AI product initiatives with measurable KPIs and ROI expectations.  
• Conduct market research and competitive analysis on AI adoption within the iGaming industry.  
• Build business cases for new AI solutions and prioritize initiatives based on impact, feasibility, and strategic value.  

Product Ownership 

• Own the end-to-end lifecycle of AI products from ideation through deployment and continuous improvement.  
• Create and manage product backlogs, user stories, acceptance criteria, and release plans.  
• Work closely with Data Science, MLOps, Engineering, UX, and Business teams to ensure successful delivery.  
• Prioritize features and experiments based on business objectives and data-driven insights.  
• Define MVPs and phased delivery strategies for AI products.  

AI & Data Collaboration 

• Collaborate with data scientists and ML engineers to understand model capabilities, limitations, and performance metrics.  
• Define requirements for machine learning models, data pipelines, and AI-driven workflows.  
• Ensure AI solutions are aligned with operational, compliance, and business requirements.  
• Participate in model evaluation, validation, and production readiness reviews.  
• Support MLOps and model governance processes.  

Stakeholder Management 

• Act as the primary liaison between business stakeholders and technical AI teams.  
• Gather requirements from executive leadership, product teams, operations, marketing, CRM, trading, and compliance departments.  
• Present product vision, roadmap updates, and performance metrics to senior management.  
• Facilitate workshops and discovery sessions to identify new AI use cases.  

Performance & Analytics 

• Define product success metrics and monitor AI product performance.  
• Measure model impact through business KPIs, including:  
• Revenue uplift  
• Customer retention  
• Churn reduction  
• Fraud prevention effectiveness  
• Responsible gaming outcomes  
• Operational efficiency improvements  
• Drive continuous optimization through experimentation and A/B testing.  

 Example AI Products within the AI Lab 

The AI Product Owner may oversee initiatives such as: 

• Personalized casino and sportsbook recommendation engines  
• Customer Lifetime Value (CLV) prediction models  
• Churn prediction and retention optimization systems  
• Fraud and anomaly detection platforms  
• Responsible gaming monitoring solutions  
• CRM segmentation and next-best-action models  
• AI-powered customer support assistants  
• Sports analytics and AI scouting platforms  
• Large Language Model (LLM) applications and internal AI copilots  
• Automated reporting and business intelligence solutions  

Requirements

• Bachelor's degree in Computer Science, Data Science, Engineering, Business Analytics, Mathematics, Economics, or a related field.  
• Master's degree is considered an advantage.  
• 4+ years of Product Management or Product Ownership experience.  
• 2+ years working directly with AI, Machine Learning, Data Science, or Analytics teams.  
• Experience managing technical products with complex data and software components.  
• Experience working in Agile/Scrum environments.  
• Experience in iGaming, Sports Betting, FinTech, AdTech, or highly data-driven industries is preferred.  

Strong understanding of: 

• Machine Learning concepts  
• Predictive analytics  
• Recommendation systems  
• Classification and regression models  
• Generative AI and LLMs  
• MLOps concepts  
• Data warehousing and analytics platforms  
• API-based product architectures  
• Experimentation and A/B testing methodologies