National Occupational Standard Framework for Data and Artificial Intelligence
Saudi Arabia · Standard · Saudi Data & AI Authority (SDAIA)
Our summary
This framework establishes occupational performance standards for data and artificial intelligence roles in Saudi Arabia. It defines key tasks, required skills, knowledge, and abilities across five main classifications covering sixteen occupations. The framework serves as a reference for recruiting, developing capabilities, and creating professional standards in the data and AI sector.
Written by the Index in our own words. It is not legal advice; the official text is the authority.
Inside this policy
Every section of this policy in order. Our notes summarise each one in plain language and do not quote the official text, which is the authority.
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10 sections
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1 Introduction
SDAIA is the government authority responsible for managing data and artificial intelligence in Saudi Arabia and serves as the national reference body for developing and handling these fields. The Kingdom aims to become a global leader in data and AI through building domestic expertise and capabilities. SDAIA has issued this occupational standard framework as a baseline reference for professionals and organizations to standardize and improve practices related to developing human capital in the sector.
2 An Overview of National Occupational Standards
National Occupational Standards define the performance levels that individuals must achieve in key job roles, along with required experience, knowledge, and abilities. The standards specify key tasks, competencies, knowledge, and individual talents for each occupation and apply at the national level, developed only by a relevant government-recognized agency. Organizations use these framework standards as reference guides for recruitment, job design, workforce planning, professional development, performance evaluation, and credential development.
3 Methodology for Building the National Occupational Standard
The framework was developed using three primary sources: subject matter experts, job postings, and experiences from other authorities including the US, UK, Canada, Australia, and Singapore. It incorporates international best practices from business sector job postings, scholarly literature and research reports, following a methodology that included working with experts, defining occupations, reviewing methodologies, collecting data, adopting an approved methodology, classifying data, and obtaining government ministry accreditation. The resulting framework includes five main classifications, ten specialty areas, and sixteen occupations, validated in three stages by SDAIA experts and national industry professionals from digital, financial, health and education sectors.
4 Classification of data and AI occupations
Data and AI occupations are classified into five categories (Business and Leadership; Governance Management and Risk Analytics; Data Science and Analytics; Engineering and Architecture; and Research), containing ten specialty areas and sixteen occupations, each defined by key tasks, skills, knowledge, and abilities. Each of the sixteen occupations is assigned a unique code using initial letters of the occupation name and field designation (D for Data, AI for Artificial Intelligence), and each occupation's job card specifies its tasks, knowledge, skills, and abilities as performance requirements.
5 Job Card Details
5 sections
5.1 Business and Leadership Category
This section defines roles and competencies in the Business and Leadership Category: Chief Data Officer (senior executive overseeing data strategy, governance, infrastructure, and quality with 27 primary and 8 additional tasks; requiring bachelor's degree in computer science or related field, preferably master's or doctorate in data science or business management, prior data technology/management/leadership experience, 11 required skills, and 15 knowledge and ability areas); Chief Artificial Intelligence Officer (C-suite executive managing AI strategy, resource/budget allocation, governance, risk management, and team building with 28 tasks); AI leadership roles requiring skills in policy development aligned with AI objectives, risk management, and team leadership, plus knowledge of AI concepts, regulations, and emerging issues; and AI consultants (professionals with bachelor's degree in computer science or related field, preferably master's or doctorate in AI/machine learning/related fields, prior management/leadership experience) creating and implementing machine learning models, developing AI policies, assessing risks, designing solutions, and educating clients, requiring skills in AI techniques, problem solving, analytical reasoning, and project management, plus knowledge of AI ethics, programming languages, statistical analysis, computer vision, natural language processing, and deep learning.
5.2 Governance Management and Risk Analytics Category
This section defines competencies for roles in governance management and risk analytics, including AI ethicists, AI risk analysts, data quality analysts, and data stewards. AI ethicists must hold a bachelor's degree in computer science or related field (master's or doctoral in AI or data science preferred) and conduct ethics reviews, assess biases and discrimination, evaluate regulatory compliance, and develop ethical guidelines while understanding AI policies, laws, ethical frameworks, and cultural regulations. AI risk analysts and governance professionals require degrees in computer science or related disciplines (master's or doctoral in machine learning, data science, or quantitative fields preferred) and must identify and manage risks, perform assessments and continuous monitoring, develop mitigation strategies, maintain regulatory compliance, and build organizational risk culture.
5.3 Date Science and Analytics Category
Data analysts and data scientists examine data from multiple sources to provide security and privacy insights, implement custom algorithms, and conduct statistical and machine learning analysis using skills in querying, mathematical modeling, programming, regression analysis, and predictive methods. Required competencies include knowledge of data governance, databases, access controls, and machine learning model construction and validation, with abilities in quality oversight, data visualization, code-based processing, problem decomposition, and cross-team collaboration. Typical education includes an undergraduate degree in informatics, numerical analysis, or equivalent fields, with an advanced degree in data science or quantitative disciplines usually favored.
5.4 Engineering and Architecture Category
The Engineering and Architecture Category establishes roles for data engineers, AI engineers, data architects, and data modelers. Data engineers prepare data for analytical or operational uses through pipeline design, data warehouse management, quality assurance, and security implementation, requiring knowledge of data management, warehousing, large-scale systems, and platforms like Hadoop and Spark. AI engineers design and implement machine learning solutions including model training, inference deployment, and techniques such as NLP and computer vision, with responsibilities for MLOps pipelines, compute resource management, and model monitoring.
5.5 Research Category
This section describes AI research, data engineering, data science and data analytics roles, specifying required skills including machine learning frameworks, interdisciplinary research, mathematical and statistical proficiency, and ethical AI application, along with recommended educational qualifications ranging from certificates to Master's degrees in related disciplines. Career progression is defined across five levels - fresh graduate, senior, principal, expert and chief - with experience requirements of 2-4 years for senior roles, 4-6 years for principal, 6-8 years for expert, and 8+ years for chief positions, supplemented by professional certificates from providers including DeepLearning AI, Stanford, IBM, Google, Microsoft, Harvard Business School, MIT, AWS, DAMA and others covering specializations such as deep learning, computer vision, NLP, machine learning, data engineering, and leadership development. Organizations are recommended to use this framework for talent acquisition, job development, workforce planning, performance management, and developing professional licenses and national occupational policies.
Controls
What this policy requires, forbids, grants or penalises, one control at a time and in our own words. Each control links to the official text, which is the authority. This is not legal advice.
Drafted by the Index Drafted automatically from the official text, not yet reviewed by a person.Reviewed Checked by a person against the official text.
Themes covered 5 of 35
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Risk and scope 0 of 4
- Risk classification, not covered
- Prohibited practices, not covered
- Risk management system, not covered
- Impact assessment, not covered
Data and privacy 1 of 4
- Data subject rights and consent, not covered
- Cross-border data transfer, not covered
- Copyright and training data, not covered
Transparency and records 0 of 4
- Transparency and disclosure to users, not covered
- Labelling of synthetic content, not covered
- Technical documentation, not covered
- Record keeping and logging, not covered
Technical safeguards 1 of 4
- Human oversight, not covered
- Bias and non-discrimination, not covered
- Testing and evaluation, not covered
Assurance and lifecycle 0 of 4
- Conformity assessment and certification, not covered
- Registration and notification, not covered
- Incident and breach reporting, not covered
- Post-market monitoring and corrective action, not covered
Rights and protected groups 0 of 4
- Automated decision-making rights, not covered
- Children and vulnerable groups, not covered
- Biometric identification and surveillance, not covered
- Whistleblowing, complaints and redress, not covered
Roles and organisation 3 of 6
- General purpose and foundation models, not covered
- Public sector use and procurement, not covered
- Innovation support and sandboxes, not covered
Enforcement and sectors 0 of 5
- Enforcement, penalties and regulators, not covered
- Sector rules: health, not covered
- Sector rules: finance and insurance, not covered
- Sector rules: employment, not covered
- Sector rules: education, not covered
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2 1 control
- Applies to: AnyoneDrafted by the Index
Organizations should use National Occupational Standards as reference guides for recruitment, job design, workforce planning, professional development, performance evaluation and credential development.
5.1 6 controls
- ObligationApplies to: ProviderDrafted by the Index
Chief Data Officers should create an overarching data strategy that aligns with the organization's core business goals.
- ObligationApplies to: ProviderDrafted by the Index
Chief Data Officers should oversee the data engineering, analytics, reporting and governance functions within an organization.
- ObligationApplies to: ProviderDrafted by the Index
Chief Data Officers should ensure that the organization's leadership integrates ethical data practices and principles into its mission and long-term direction.
- ObligationApplies to: ProviderDrafted by the Index
Chief Data Officers should establish a structured roadmap for governance that emphasizes data integrity, management norms, and security safeguards.
- ObligationApplies to: ProviderDrafted by the Index
Chief Data Officers should recruit, develop and lead teams with expertise in data disciplines.
- ObligationApplies to: ProviderDrafted by the Index
Chief Data Officers should work with partners and external organizations to understand emerging data strategy requirements and technology solutions.
Change history
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