From role 1.1 Data Scientist · EQF EQF7
Enhance organisational decision-making and operational responsiveness by evaluating AI adoption patterns, integrating predictive insights, and translating trends into forward-looking strategic forecasts across business functions.
7.1 Explain AI adoption patterns across organisational functions and their operational implications in areas such as marketing, operations, and product development.
Assessed by Analytical report or case study submission assessing explanation of AI adoption patterns and operational implications; evaluated for analytical depth, use of evidence, clarity of reasoning, and linkage between observed patterns and organisational impact.
7.2 Understand competitive and market dynamics affecting AI adoption using sector-specific studies and benchmarking analyses.
Assessed by Market intelligence brief evaluated for depth of insight, quality of data sources, and relevance to business function analysis; assessed for synthesis of market and technological trends and justification of identified implications.
7.3 Evaluate AI trend data and interpret their impact on products, services, and internal processes using predictive analytics and cross-functional performance metrics.
Assessed by Predictive data analysis report assessed for accuracy of interpretation, appropriate selection and use of predictive models and metrics, and robustness of results, including validation and handling of uncertainty.
7.4 Develop forward-looking forecasts and apply predictive insights to improve operational efficiency and decision-making by integrating data-driven scenario planning into organisational strategies.
Assessed by Forward-looking forecast document graded for integration of predictive insights, scenario analysis, and development of actionable recommendations; evaluated for coherence, plausibility, and strategic relevance.
7.5 Design AI-driven process improvements by identifying areas of efficiency gains or innovation opportunities.
Assessed by Process improvement proposal evaluated for feasibility, level of innovation, and potential impact; assessed for alignment with forecast outcomes, justification of proposed changes, and consideration of organisational constraints.
7.6 Assess the feasibility and business value of AI adoption initiatives through cost-benefit analysis and performance evaluation.
Assessed by Feasibility and business impact assessment report, including cost–benefit analysis and risk considerations for forecast-driven initiatives; evaluated for completeness, validity of assumptions, and strength of evidence-based conclusions. DATA PROCESSING & ANALYSIS [1]
7.7 Present actionable AI-informed strategies and forecasts to decision-makers using clear, evidence-based communication.