Data science DATS

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Applying mathematics, statistics, data mining and predictive modelling techniques to gain insights, predict behaviours and generate value from data.

SFIA 9 is in development

SFIA 9 beta due in early July 2024
SFIA 9 planned for publication October 2024

Guidance notes

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Data science is typically used for analysing high volume, high velocity and high variety data (numbers, symbols, text, sound and image).

Activities may include, but are not limited to:

integrating methods from mathematics, statistics and probability modelling using specialised programming languages, tools and techniques
sourcing and preparing data for analysis
identifying, validating and exploiting internal and external data sets generated from a diverse range of processes
developing forward-looking, predictive, real-time, model-based insights to create value and drive effective decision-making
finding, selecting, acquiring and ingesting data sources
integrating and cleaning data to make it fit for purpose
developing hypotheses and exploring data using models and analytics sandboxes
refining requirements, validating, training and evolving models over time to discover deeper insights, make predictions, or generate recommendations
using advanced analytic techniques including, but not limited to, data/text mining, machine learning, pattern matching, forecasting, visualisation, semantic analysis, sentiment analysis, network and cluster analysis, multivariate statistics, graph analysis, simulation, complex event processing, neural networks.

Understanding the responsibility levels of this skill

Where lower levels are not defined...

Specific tasks and responsibilities are not defined because the skill requires a higher level of autonomy, influence, and complexity in decision-making than is typically expected at these levels. You can use the essence statements to understand the generic responsibilities associated with these levels.

Where higher levels are not defined...

Responsibilities and accountabilities are not defined because these higher levels involve strategic leadership and broader organisational influence that goes beyond the scope of this specific skill. See the essence statements.

Developing skills and demonstrating responsibilities related to this skill

The defined levels show the incremental progression in skills and reponsibilities.

Where lower levels are not defined...

You can develop your knowledge and support others who do have responsibility in this area by:

Learning key concepts and principles related to this skill and its impact on your role
Performing related skills (see the related SFIA skills)
Supporting others with tasks (generic examples are provided by the essence statements for each level)

Where higher levels are not defined...

You can progress by developing related skills which are better suited to higher levels of organisational leadership.

Levels

Defined at these levels:

Click to learn why SFIA skills are not defined at all 7 levels.

Show/hide extra descriptions and levels.

Level 1

Level 1 - Follow: Essence of the level: Performs routine tasks under close supervision, follows instructions, and requires guidance to complete their work. Learns and applies basic skills and knowledge.

Data science: Level 2

Level 2 - Assist: Essence of the level: Provides assistance to others, works under routine supervision, and uses their discretion to address routine problems. Actively learns through training and on-the-job experiences.

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Under guidance, applies given data science techniques to data.

Analyses and reports findings and remediates simple issues, using algorithms implemented in standard software frameworks and tools.

Data science: Level 3

Level 3 - Apply: Essence of the level: Performs varied tasks, sometimes complex and non-routine, using standard methods and procedures. Works under general direction, exercises discretion, and manages own work within deadlines. Proactively enhances skills and impact in the workplace.

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Applies existing data science techniques to new problems and datasets using specialised programming techniques.

Selects from existing data sources and prepares data to be used by data science models.

Evaluates the outcomes and performance of data science models. Identifies and implements opportunities to train and improve models and the data they use.

Publishes and reports on model outputs to meet customer needs and conforming to agreed standards.

Data science: Level 4

Level 4 - Enable: Essence of the level: Performs diverse complex activities, supports and supervises others, works autonomously under general direction, and contributes expertise to deliver team objectives.

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Investigates the described problem and dataset to assess the usefulness of data science and analytics solutions.

Applies a range of data science techniques and uses specialised programming languages. Understands and applies rules and guidelines specific to the industry, and anticipates risks and other implications of modelling.

Selects, acquires and integrates data for analysis. Develops data hypotheses and methods and evaluates analytics models. Advises on the effectiveness of specific techniques based on project findings and comprehensive research.

Contributes to the development, evaluation, monitoring and deployment of data science solutions.

Data science: Level 5

Level 5 - Ensure, advise: Essence of the level: Provides authoritative guidance in their field and works under broad direction. Accountable for achieving workgroup objectives and managing work from analysis to execution and evaluation.

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Plans and drives all stages of the development of data science and analytics solutions.

Provides expert advice to evaluate the problems to be solved and the need for data science solutions. Identifies what data sources to use or acquire.

Specifies and applies appropriate data science techniques and specialised programming languages.

Reviews the benefits and value of data science techniques and tools and recommends improvements. Contributes to developing policy, standards and guidelines for developing, evaluating, monitoring and deploying data science solutions.

Data science: Level 6

Level 6 - Initiate, influence: Essence of the level: Has significant organisational influence, makes high-level decisions, shapes policies, demonstrates leadership, fosters organizational collaboration, and accepts accountability in key areas.

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Leads the introduction and use of data science to drive innovation and business value.

Develops organisational policies, standards, and guidelines for data science.

Sets direction and leads in the introduction and use of data science techniques, methodologies and tools. Leads the development of organisational capabilities for data science.

Plans and leads strategic, large and complex data science initiatives to generate insights, create value and drive decision-making.

Level 7

Level 7 - Set strategy, inspire, mobilise: Essence of the level: Operates at the highest organisational level, determines overall organisational vision and strategy, and assumes accountability for overall success.