data scientist
Snapshot
Unlock the power of data and shape the future with a career as a data scientist. This role combines analytical thinking, technical skills, and communication to transform raw data into actionable insights that drive strategic decisions.
As a data scientist at Career Band 5 (Leadership & Strategy), you’ll be at the forefront of data analysis, responsible for identifying valuable data sources, managing and merging large datasets, and ensuring data accuracy. You’ll build sophisticated mathematical models, create compelling visualizations, and effectively communicate your findings to both technical and non-technical audiences, ultimately recommending data-driven solutions and strategies.
- • Discover and interpret data from various sources.
- • Build and refine mathematical models to uncover patterns and predict outcomes.
- • Communicate complex data insights clearly to specialists and non-specialists.
Unlock the power of data and shape the future with a career as a data scientist. This role combines analytical thinking, technical skills, and communication to transform raw data into actionable insights that drive strategic decisions.
Could data scientist fit you?
Answer three quick questions. This is not a full assessment — it is a teaser to help you decide whether to compare your profile.
Do you enjoy tasks that require Analytical Thinking?
Do you enjoy tasks that require Integrity?
Do you enjoy tasks that require Attention to Detail?
Future Outlook for data scientist
The outlook for data scientist is exceptionally stable. While AI tools will assist with daily tasks, the core of this role relies on human judgment, resulting in a high resilience score of 81.8%.
How are these scores calculated?
The Resilience Score (0–100) estimates how structurally protected this occupation is from automation and AI disruption, based on task-level analysis. Higher scores mean more human-judgment-intensive tasks. AI Exposure shows the estimated percentage of task hours that current AI capabilities could affect. These are model-derived structural indicators, not predictions about individual job security.
How could data scientist change as AI adoption grows?
Human judgement, trust, and context remain strong protectors for this role.
How could data scientist change as AI adoption grows?
Human judgement, trust, and context remain strong protectors for this role.
How AI may change this role
Deterministic, model-based interpretation of current role signals — not a guarantee of replacement.
What still depends on people
This role remains strongly human-led where build recommender systems depends on trust, nuance, and real-world judgement.
Where AI may become a co-pilot
AI is more likely to assist supporting tasks such as develop data processing applications, documentation, search, and workflow coordination.
Tasks most exposed to automation
Automation pressure appears selective rather than broad, with the strongest signal currently coming from Generative AI.
Detailed Analysis Vital Signs, AI Vectors & Megatrends
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Vital Signs, AI Vectors & Megatrends
Vital Signs
AI Exposure Vectors
0-100%Exposure to content generation, creative augmentation, and large language model tools
Exposure to workflow automation, decision-support software, and process digitisation
Exposure to AI-assisted analysis, pattern recognition, and predictive modelling tasks
Exposure to physical automation, robotics, and sensor-driven task displacement
Megatrend Signals
0-100%Model-derived scores. Indicates structural exposure to megatrends, not direct demand.
Technical Details
NexFuture™ v2.0 combines O*NET ability and activity profiles with ESCO skill group distributions and six global megatrend signals. Scores are probabilistic estimates, not guarantees. See the NexFuture™ Methodology White Paper for full details.
What people in this role usually do
Digital Technology
A typical day as a data scientist
09 09:00 · Morning build recommender systems
10 10:30 · Mid-morning develop data processing applications
12 12:00 · Midday design database scheme
14 14:00 · Afternoon establish data processes
15 15:30 · Late afternoon manage data collection systems
17 17:00 · Wrap-up manage intellectual property rights
Task order is illustrative. Individual days vary.
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data mining
The methods of artificial intelligence, machine learning, statistics and databases used to extract content from a dataset.
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data models
The techniques and existing systems used for structuring data elements and showing relationships between them, as well as methods for interpreting the data structures and relationships.
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information categorisation
The process of classifying the information into categories and showing relationships between the data for some clearly defined purposes.
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information extraction
The techniques and methods used for eliciting and extracting information from unstructured or semi-structured digital documents and sources.
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online analytical processing
The online tools which analyse, aggregate and present multi-dimensional data enabling users to interactively and selectively extract and view data from specific points of view.
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query languages
The field of standardised computer languages for retrieval of information from a database and of documents containing the needed information.
- data engineering
- data ethics
- data science
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manage findable accessible interoperable and reusable data
Produce, describe, store, preserve and (re) use scientific data based on FAIR (Findable, Accessible, Interoperable, and Reusable) principles, making data as open as possible, and as closed as necessary.
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perform scientific research
Gain, correct or improve knowledge about phenomena by using scientific methods and techniques, based on empirical or measurable observations.
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apply research ethics and scientific integrity principles in research activities
Apply fundamental ethical principles and legislation to scientific research, including issues of research integrity. Perform, review, or report research avoiding misconducts such as fabrication, falsification, and plagiarism.
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promote open innovation in research
Apply techniques, models, methods and strategies which contribute to the promotion of steps towards innovation through collaboration with people and organizations outside the organisation.
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integrate gender dimension in research
Take into account in the whole research process the biological characteristics and the evolving social and cultural features of women and men (gender).
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conduct research across disciplines
Work and use research findings and data across disciplinary and/or functional boundaries.
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normalise data
Reduce data to their accurate core form (normal forms) in order to achieve such results as minimisation of dependency, elimination of redundancy, increase of consistency.
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use data processing techniques
Gather, process and analyse relevant data and information, properly store and update data and represent figures and data using charts and statistical diagrams.
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establish data processes
Use ICT tools to apply mathematical, algorithmic or other data manipulation processes in order to create information.
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use databases
Use software tools for managing and organising data in a structured environment which consists of attributes, tables and relationships in order to query and modify the stored data.
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perform data cleansing
Detect and correct corrupt records from data sets, ensure that the data become and remain structured according to guidelines.
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implement data quality processes
Apply quality analysis, validation and verification techniques on data to check data quality integrity.
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draft scientific or academic papers and technical documentation
Draft and edit scientific, academic or technical texts on different subjects.
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disseminate results to the scientific community
Publicly disclose scientific results by any appropriate means, including conferences, workshops, colloquia and scientific publications.
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publish academic research
Conduct academic research, in universities and research institutions, or on a personal account, publish it in books or academic journals with the aim of contributing to a field of expertise and achieving personal academic accreditation.
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write scientific publications
Present the hypothesis, findings, and conclusions of your scientific research in your field of expertise in a professional publication.
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operate open source software
Operate Open Source software, knowing the main Open Source models, licensing schemes, and the coding practices commonly adopted in the production of Open Source software.
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build recommender systems
Construct recommendation systems based on large data sets using programming languages or computer tools to create a subclass of information filtering system that seeks to predict the rating or preference a user gives to an item.
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develop data processing applications
Create a customised software for processing data by selecting and using the appropriate computer programming language in order for an ICT system to produce demanded output based on expected input.
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handle data samples
Collect and select a set of data from a population by a statistical or other defined procedure.
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collect ICT data
Gather data by designing and applying search and sampling methods.
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synthesise information
Critically read, interpret, and summarise new and complex information from diverse sources.
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manage research data
Produce and analyse scientific data originating from qualitative and quantitative research methods. Store and maintain the data in research databases. Support the re-use of scientific data and be familiar with open data management principles.
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manage data collection systems
Develop and manage methods and strategies used to maximise data quality and statistical efficiency in the collection of data, in order to ensure the gathered data are optimised for further processing.
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deliver visual presentation of data
Create visual representations of data such as charts or diagrams for easier understanding.
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communicate with a non-scientific audience
Communicate about scientific findings to a non-scientific audience, including the general public. Tailor the communication of scientific concepts, debates, findings to the audience, using a variety of methods for different target groups, including visual presentations.
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interpret current data
Analyse data gathered from sources such as market data, scientific papers, customer requirements and questionnaires which are current and up-to-date in order to assess development and innovation in areas of expertise.
Skill DNA
Work personality traits and values that define this role
See whether this role fits your Career DNA
Take the free Career DNA assessment to see how data scientist aligns with your interests, work style, and future path. In less than 10 minutes, you will get a personalized fit signal and a roadmap for what to do next.
Scan to continue on mobileGrowth Pathways & Similar Roles
Explore typical career progression paths, adjacent skills, and similar roles to plan your next transition.
Where does data scientist fit?
Similarity scores based on skill overlap from ESCO data.
Frequently asked questions
- What kind of background is helpful for a data scientist role at this level?
- A strong foundation in mathematics, statistics, or computer science is typically required. Experience with programming languages like Python or R, and familiarity with data visualization tools, is also beneficial. Given this is a Leadership & Strategy band, experience leading projects and presenting findings to stakeholders is highly valued.
- How important are communication skills in this role?
- Communication is crucial. You’ll be translating complex data findings into understandable insights for a variety of audiences, from fellow data scientists to business leaders. The ability to clearly articulate your methodology and recommendations is essential.
- Is it common to work as a freelance data scientist?
- While primarily an employee-based role, freelancing opportunities for data scientists are also common. This offers flexibility, but typically involves managing your own projects and client relationships.