• Own the strategic direction of the device data and performance platform • Define how Palmetto connects with hardware manufacturers through integration partnerships • Shape data models for performance monitoring and alerting • Build workflows for field service and asset management teams • Collaborate across Asset Management, Field Service, Operations, Customer Support, Finance, and Engineering • Drive alerting logic and performance insights • Ensure monitoring coverage with new asset types • Own product strategy, OKRs, roadmap, and quarterly planning • Encourage AI use for discovery and learning • Manage stakeholder expectations with clear communication

• Manage and prioritize the product backlog, balancing legal/regulatory requirements, bug fixes, enhancements, and continuous product evolution; • Translate changes in labor legislation into clear requirements for the development team; • Write user stories and acceptance criteria; • Lead backlog refinements and support the technical team in understanding requirements; • Work in partnership with Maintenance, Support, Customer Success, and other stakeholders to define priorities and ensure value delivery; • Act as a bridge between the business, customers, and development, translating needs into product solutions; • Gather customer needs and validate opportunities for product evolution; • Monitor metrics and use data to support product decisions.

• Ideate, develop and improve machine learning and statistical models that drive Swish’s core algorithms for producing state-of-the-art sports betting products. • Develop contextualized feature sets using specific domain knowledge in soccer. • Contribute to all stages of model development, from creating proof-of-concepts and beta testing, to partnering with data engineering and product teams to deploy new models. • Strive to constantly improve model performance using insights from rigorous offline and online experimentation. • Analyze results and outputs to assess model performance and identify model weaknesses for directing development efforts. • Adhere to software engineering best practices and contribute to shared code repositories. • Document modeling work and present to stakeholders and other technical and non-technical partners.

• Design, build, and validate predictive models (e.g. lead scoring, propensity modelling, forecasting, risk prediction) that inform operational and strategic decisions. • Develop and deploy generative AI solutions, including LLM-powered agents, retrieval-augmented generation (RAG) pipelines, and prompt engineering workflows to automate and enhance business processes. • Translate business problems into well-scoped analytical and modelling projects, working with stakeholders to define success criteria and measurable outcomes. • Perform feature engineering, model selection, hyperparameter tuning, and rigorous evaluation using appropriate statistical and ML techniques. • Productionise models into scalable, maintainable pipelines, collaborating with data engineering to integrate outputs into downstream systems (e.g. CRM, BI tools, operational dashboards). • Monitor model performance post-deployment, manage model drift, and implement retraining strategies. • Communicate findings and model outputs to non-technical stakeholders through clear visualisations, written summaries, and presentations. • Stay current with developments in applied ML and generative AI, and contribute to the team’s knowledge-sharing and capability-building efforts. • Provide guidance, direction, and oversight to one direct report within the Data, Insights & AI team.