Principal Specialist Data Science & Artificial Intelligence

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About the Role Maaden is seeking a Principal Specialist Data Science & Artificial Intelligence to lead advanced data science and analytics initiatives within the Agentic AI department. This full-time role in Saudi Arabia focuses on translating complex business challenges into scalable analytical solutions, AI-enabled insights, predictive models, and decision intelligence products. The specialist will contribute to building enterprise capabilities that leverage trusted data, machine learning, automation, and agentic AI concepts to enhance productivity, operational performance, and business decision-making. Key Responsibilities Translate business priorities, operational challenges, and use cases into clear data science problem statements, analytical approaches, and measurable outcomes. Design and deliver predictive, prescriptive, diagnostic, and exploratory analytics solutions that support decision-making across business and operational domains. Develop machine learning models, statistical analyses, simulations, optimization approaches, and advanced analytics products using enterprise data assets. Ensure analytical outputs are interpretable, actionable, repeatable, and aligned with business value, adoption needs, and governance requirements. Support the development of agentic AI use cases by defining data, model, workflow, evaluation, and decision-support requirements. Collaborate with AI engineers, platform teams, data engineers, and business stakeholders to embed analytics and machine learning into autonomous or semi-autonomous workflows. Define evaluation approaches for AI-enabled insights, model performance, business impact, human oversight, and continuous improvement. Promote responsible use of agentic AI by ensuring transparency, explainability, control, auditability, and fit-for-purpose model deployment. Create reusable analytical assets, data science patterns, feature logic, model components, and decision intelligence products that can scale across business domains. Partner with data platform, governance, and architecture teams to ensure data products are trusted, well-documented, reusable, and operationally maintainable. Integrate analytics into dashboards, workflows, applications, copilots, and AI agents where decision support can improve speed, quality, and productivity. Maintain clear documentation covering assumptions, data sources, model logic, limitations, controls, and expected business usage. Apply appropriate data science governance practices for model validation, performance monitoring, bias checks, versioning, deployment readiness, and lifecycle management. Define success measures, impact metrics, adoption criteria, and benefit tracking approaches for analytics and AI-enabled solutions. Monitor solution performance after deployment and recommend improvements based on usage, feedback, model drift, data quality, and business impact. Ensure data science and analytics solutions remain compliant with data governance, cybersecurity, privacy, and responsible AI expectations. Technical Leadership and Advisory Provide expert guidance to analysts, data scientists, engineers, and business teams on analytical methods, model design, evaluation, and adoption. Advise stakeholders on what analytics or AI can realistically solve, required data readiness, implementation complexity, and expected value. Review analytical designs, model outputs, assumptions, and recommendations to ensure technical quality and business relevance. Communicate insights, risks, limitations, and recommended actions clearly to technical, operational, and executive stakeholders. Qualifications and Experience A minimum of 10 years of relevant experience in data science and artificial intelligence. Success Factors Successful performance in this role will be demonstrated by the effective translation of business problems into clear data science use cases and measurable value outcomes. This includes the delivery of predictive, prescriptive, and decision intelligence solutions with strong technical quality and practical business relevance. Success also involves supporting Agentic AI use cases with trusted data, robust models, evaluation methods, controls, and human oversight, as well as ensuring analytics and AI products are reusable, documented, governed, monitored, and aligned with responsible AI expectations. Ultimately, the role aims to provide stakeholders with clear insights, recommendations, and decision support that improve productivity, performance, and adoption.