Data, analytics and AI
Makes data usable, tests assumptions and builds models and reports that decisions rely on, distinguished by conclusions that must be statistically sound and explainable to non specialists.
Example roles: data analyst, business intelligence specialist, data scientist, data engineer, ai specialist, reporting specialist
Base profile of the family
Every job in this family inherits this profile and deviates from it with a limited number of additions, adjustments and removals. The target levels below apply to the medior level.
| T | Skill | Target level | Weight | Knockout |
|---|---|---|---|---|
| V | Data analysis | N4 | 5 | yes |
| K | Basic statistical interpretation | N4 | 5 | yes |
| A | Numerical reasoning | N4 | 5 | yes |
| V | Assessing data quality | N4 | 4 | yes |
| V | Programming | N3 | 4 | yes |
| V | Data visualisation | N4 | 4 | no |
| V | Reporting and dashboard building | N4 | 4 | no |
| V | Data modelling | N4 | 4 | no |
| K | Machine learning fundamentals | N3 | 4 | no |
| V | Critical thinking and source evaluation | N4 | 4 | no |
| V | Visual communication and data storytelling | N4 | 4 | no |
| V | Testing hypotheses | N3 | 3 | no |
| V | Data management and storage | N3 | 3 | no |
| V | Data governance and ownership | N3 | 3 | no |
| K | GDPR and privacy in practice | N3 | 3 | no |
| V | Selecting and implementing AI applications | N3 | 3 | no |
| V | AI governance and responsible use | N3 | 3 | no |
| V | Recognising AI risk and bias | N3 | 3 | no |
| V | Systems thinking | N3 | 3 | no |
| V | Methodical working | N3 | 3 | no |
| V | Advisory skill | N3 | 3 | no |
| G | Curiosity and exploring | N3 | 3 | no |
| V | multidisciplinary collaboration | N3 | 2 | no |
| G | Dealing with ambiguity | N3 | 1 | no |
Seniority adjustment
These values are added to every target level of the family and then clamped to 1 to 5.
Jobs 20
Builds applications on existing AI services and language models, connects them to business data and controls the risks in their output.
Sets the product direction of AI applications, weighs the feasibility and risk of models against user value and works with data scientists and engineers.
Connects business questions with analytics capacity, decides which issues are worth analysing and makes sure outcomes land in decisions.
Advises clients on the setup of reporting and analytics, selects suitable solutions and guides adoption among users.
Unlocks source systems in data models and builds dashboards and reports with which departments can track their performance structurally.
Sets the data strategy of an organisation, establishes governance and teams and accounts for the return on data use to the executive board.
Investigates datasets to answer organisational questions, checks the quality of the data and delivers substantiated insights and overviews.
Designs the coherence of data, models and platforms in an organisation and sets standards that build teams adhere to.
Advises clients on data strategy and analytics setup, translates business questions into a data plan and guides implementation at the client organisation.
Builds and maintains data pipelines and storage so data from source systems becomes reliably, timely and repeatably available for analysis.
Measures and monitors data quality in source systems, traces errors and inconsistencies and works with data owners on structural improvement.
Develops statistical and learning models on business data, tests their reliability and translates outcomes into usable conclusions.
Keeps definitions, quality and ownership of data in order and holds departments to agreements on recording and use of data.
Analyses and visualises spatial data in a geographic information system, builds maps and spatial analyses for policy, planning or infrastructure.
Takes models to production and keeps them working by automating and managing training, deployment and monitoring of model performance.
Builds and maintains the infrastructure with which machine learning models are trained, deployed and monitored, and automates retraining when models grow stale.
Develops and researches tests and questionnaires, establishes reliability and validity and sets norms for responsible use.
Turns research questions into quantitative analyses, designs surveys or experiments and statistically tests whether observed relationships are reliable.
Compiles periodic reports and management information, checks the figures and makes sure the explanation is traceable for readers.
Selects and applies statistical methods to research questions, guards the underlying assumptions and accounts for the reliability of results.