Principal Data Product Manager - Machine Learning / AI

As a Principal Data Product Manager – ML/AI, you will own the vision, strategy, and roadmap of our internal Machine Learning and AI initiatives, focusing on the “what” and “why” behind data-driven solutions. You’ll partner closely with our Director of Engineering/Research - Machine Learning and their engineering teams to translate business problems into technical capabilities that will generate clear business outcomes. Your work will streamline how we ideate, develop, and deploy machine learning models—ranging from predictive analytics, personalization, and fraud detection to real-time recommendations—ensuring our customers benefit from cutting-edge, scalable, and reliable AI-powered features. By fostering strong cross-functional collaboration, you will help shape the company’s data-driven culture, accelerate innovation, and drive measurable impact on Super's growth and customer satisfaction.

1. Product strategy & vision

• Own the long-term product vision and roadmap for ML/AI solutions, in alignment with Superbet’s strategic objectives.
• Identify and champion high-value ML/AI use cases, balancing quick wins with transformational initiatives that unlock new business potential.

2. Defining the “What” and “Why”

• Gather and synthesize stakeholder requirements (e.g., from Product, Marketing, Operations) into clear problem statements, business impact, and success metrics.
• Work with Data Scientists and ML Engineers to ensure solutions are feasible and viable, focusing on outcomes rather than just outputs.

3. Roadmap & stakeholder management

• Own and maintain a prioritized ML/AI product roadmap, aligning it with company goals, resource availability, and technical constraints.
• Collaborate with senior leaders and internal stakeholders to manage expectations, refine requirements, and secure support for new initiatives.

4. Cross-functional collaboration

• Partner closely with the Director of ML/AI (and associated engineering teams) to transform product requirements into robust, scalable, and efficient ML pipelines.
• Coordinate with Data Engineering, Insights, and other Product teams to ensure smooth data flows, model deployment, and feedback loops.
• Drive alignment on the usage of ML/AI technologies and best practices across the organization.

5. Execution & delivery

• Communicate product requirements, user stories, and acceptance criteria to engineering teams; ensure clarity of priorities and iterative delivery.
• Champion Agile methodologies (or relevant frameworks) to maintain focus on delivering incremental, high-impact features.
• Guarantee each project has a well-defined Problem to Solve, Business Impact, and Success Measurement, continuously tracking progress and iterating as needed.

6. Performance & impact tracking

• Define KPIs and success metrics for ML/AI solutions (e.g., model accuracy, time-to-delivery, user adoption, etc).
• Analyze performance data to derive actionable insights, optimize outcomes, and refine product roadmap.
• Communicate progress, challenges, and results to key stakeholders, including executive leadership.

7. Domain expertise & thought leadership

• Stay current with the latest trends in machine learning, AI research, and MLOps best practices.
• Advocate for a data-driven culture, evangelizing ML/AI solutions and sharing success stories that highlight their value.
• Identify opportunities for new or improved AI capabilities that can enhance customer experiences, trust, and satisfaction.

8. Communication & evangelism

• Serve as an internal ambassador for ML/AI, presenting product updates, demos, and vision statements to cross-functional teams and executives.
• Help non-technical stakeholders understand complex AI concepts in plain language, fostering trust, collaboration, and widespread adoption.

1. Education & experience

• Bachelor’s or Master’s degree in Business, Computer Science, Data Science, or a related field (or equivalent practical experience).
• 5–7+ years of product management experience, ideally focusing on data or AI/ML products.

2. Technical & domain expertise

• Familiarity with machine learning pipelines, model lifecycle management, and popular ML frameworks (e.g., TensorFlow, PyTorch).
• Understanding of cloud computing platforms (AWS preferred), containerization (Docker/Kubernetes), and modern data stacks.
• Demonstrated ability to collaborate with technical teams (Data Scientists, ML Engineers) in an Agile environment.

3. Product management skills

• Track record of delivering data/AI-based products from concept to launch, with clear metrics for success.
• Strong backlog prioritization, stakeholder management, and requirement-gathering capabilities.
• Skilled at translating technical complexities into user-centric language and actionable product roadmaps.

4. Strong differentiators

• Experience in gaming, sports betting, or entertainment industries, focusing on personalisation, fraud detection, or predictive analytics use cases.
• Familiarity with training and fine-tuning large-scale foundation models (GPT, BERT, etc.) for various applications.
• Background in MLOps practices (e.g., MLflow, Airflow, Spark) and/or advanced ML model deployment strategies.