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L6Apprenticeship3371 approved provider
The Level 6 Data Scientist (Integrated Degree), and the 1 provider delivering it.
Working in a team to find ways to improve an organisation's processes.
About this apprenticeship
What this apprenticeship covers
Apprentices learn to extract meaning from complex datasets using statistical analysis, machine learning, predictive modelling, and programming. The programme covers data sourcing, manipulation, and engineering, as well as model building and validation. Alongside technical skills, apprentices develop the ability to identify data bias, handle personal data in line with privacy regulations, and present findings clearly to both technical and non-technical stakeholders. Planning and resource management are also part of the standard, reflecting the expectation that data scientists operate at both strategic and operational levels.
Day-to-day responsibilities
Week to week, an apprentice will write and maintain code to clean, transform, and analyse datasets, build and test statistical or machine learning models, and document their methods and results. They will attend project meetings with domain experts and business stakeholders, translate analytical findings into plain-language reports or visualisations, and contribute to decisions about data pipelines or tooling. Tools typically include Python or R, SQL, and data visualisation libraries, though the specific stack depends on the employer.
Career outlook
On completion, graduates typically move into data scientist, applied scientist, or machine learning engineer roles. With experience, common progression routes include senior data scientist, lead data scientist, or specialist positions in areas such as natural language processing or computer vision. Some move into data strategy or management. Employers hiring at this level span financial services, healthcare, retail, central and local government, technology firms, and consultancies. The integrated degree element means graduates leave with both a bachelor's degree and occupational competence, which strengthens their position for roles that require formal academic qualifications.
1 approved provider
Sorted by achievement rate.
Anglia Ruskin University ARU
Employer: 3.0
Anglia Ruskin University (ARU) is an innovative UK university offering a wide portfolio of learning ...
Career outcomes
Roles after completion
Graduates typically move into positions such as Junior Data Scientist, Data Scientist, or Graduate Data Analyst. Some take on roles with a stronger engineering focus, such as Machine Learning Engineer or Data Engineer, depending on which technical areas they specialised in during the programme. Others step into Applied Scientist or Quantitative Analyst roles, particularly in organisations where statistical modelling sits at the core of the work.
Progression paths
Within three to five years, many move into mid-level Data Scientist or Senior Data Scientist roles, taking ownership of end-to-end projects and influencing how their organisation uses data at a strategic level. Beyond that, the path typically splits: a leadership track leads to Principal Data Scientist, Head of Data Science, or Chief Data Officer, while a deep-specialist track leads to expert positions in machine learning research, AI engineering, or statistical modelling, often with responsibility for setting technical standards across a team or organisation.
Where these roles sit
Demand spans a wide range of industries. Financial services firms, insurers, and banks are consistent hirers, as are technology companies and software businesses of all sizes. Retailers with large customer datasets, NHS trusts and health analytics organisations, government departments, and consultancies working across the public and private sectors all recruit at this level. Employers range from large corporates with dedicated data teams to smaller organisations where a data scientist may be one of very few specialists.
How it's assessed
How the apprenticeship is assessed
Because this is an integrated degree apprenticeship, assessment is woven through the programme rather than sitting entirely at the end. The apprentice studies towards a bachelor's degree while working in a data science role, with academic and workplace learning reinforcing each other throughout. Before final assessment, there is a readiness check, often called a gateway, at which the employer, training provider and apprentice confirm that the required knowledge, skills and behaviours have been developed sufficiently. Final assessment then confirms occupational competence across the technical and professional demands of the role. Assessment arrangements for many standards are currently being updated, so check the standard's gov.uk page for the current specification.
What learners need to prepare
Building a strong body of workplace evidence from early in the programme makes the final stages considerably less pressured. Apprentices should keep records of real projects, noting how they sourced and manipulated data, applied statistical or machine learning methods, handled data ethically, and communicated findings to different audiences. Regular reviews with both the employer and the training provider help track progress against the standard's knowledge, skills and behaviours. Leaving evidence-gathering until close to gateway creates unnecessary risk, so consistent record-keeping throughout the programme is strongly advisable.
Choosing a provider
What good looks like
Look for providers with an achievement rate above 75% on their FATP profile, given the academic weight of an integrated degree at this level. Strong providers will have clear relationships with employers who are actually using data science in production, not just running analytics reporting. Check that the curriculum references current tooling such as Python, SQL, cloud platforms, and modern ML frameworks, and that tutors or lecturers have recent practitioner experience rather than purely academic backgrounds. Apprentice satisfaction scores above 80% are worth noting, particularly comments about real-world project work and access to live datasets.
Red flags to watch for
Be cautious if a provider cannot explain how off-the-job training connects to genuinely applied data science problems. A high volume of enrolments combined with a declining achievement rate can signal that pastoral and academic support is stretched. If the provider's curriculum materials reference outdated tools or make no mention of ML model validation, bias assessment, or data ethics regulation such as UK GDPR, the programme may not reflect what the role actually demands. Vague answers about employer engagement or an inability to point to alumni working in data science roles are also warning signs.
Questions to ask before you commit
- What programming languages, ML frameworks and cloud platforms does the curriculum cover, and how recently were these reviewed?
- How does the provider assess students on model building, validation and statistical analysis, rather than just theory?
- Can you show examples of capstone or dissertation projects that apprentices have completed using real employer data?
- How does the programme address data ethics, bias identification and UK GDPR compliance?
- What is the current achievement rate for this specific standard, and how has it changed over the past two years?
- How large are cohorts, and what is the tutor-to-apprentice ratio for technical mentoring?
- What support exists for apprentices who need to balance degree-level academic demands with their day-to-day role?
Common questions
What qualifications or experience do I need to start a data scientist apprenticeship?
Employers set their own entry requirements, but most look for strong A-level results, particularly in maths or a related subject, or equivalent prior qualifications. Some employers accept relevant work experience in place of formal grades. Because this is an integrated degree programme, applicants typically need to meet the entry criteria for the degree element as well. Candidates who already hold a degree at this level are not eligible for the apprenticeship funding.
How long does the apprenticeship take and how does learning fit around work?
The typical duration is 36 months. Apprentices are employed throughout, applying their learning on real data problems from day one. A portion of working time is dedicated to off-the-job learning, covering statistics, programming, machine learning and data ethics. The exact proportion is subject to ongoing policy changes under Skills England reforms, so check the current specification on the Institute for Apprenticeships and Technical Education page for this standard before planning a programme.
How is the apprenticeship assessed and what does the end-point assessment involve?
Before reaching end-point assessment, the apprentice must pass through a gateway, where the employer and training provider confirm the apprentice has met all the required knowledge, skills and behaviours. The end-point assessment then tests competence independently of the training provider. Assessment models for many standards are being updated, so check the current assessment plan on gov.uk for the specific methods that apply to this standard at the time you are recruiting or enrolling.
How does funding work for employers taking on a data scientist apprentice?
The funding band for this standard is £19,000, which is the maximum that can be drawn from the apprenticeship levy or co-investment arrangement to cover training and assessment costs. Larger employers with a levy account use those funds directly. SMEs without a levy account pay 5% of costs, with the government covering the remaining 95%. Employers with fewer than 50 staff taking on a 16 to 18-year-old apprentice pay nothing. Salary costs are always the employer's responsibility.
What does a data scientist apprentice actually do during their working day?
Day-to-day work typically involves sourcing and manipulating datasets, writing code to clean and analyse data, building statistical or machine learning models, and presenting findings to both technical and non-technical colleagues. Apprentices handle private data in line with privacy regulations, identify bias in datasets, and contribute to decisions that affect operational and strategic outcomes. They often work alongside data engineers, analysts and domain experts, and are expected to engage with the wider data science community as their skills grow.
What can a data scientist apprentice move into after completing the programme?
Completing this programme results in both an apprenticeship and an integrated degree, giving graduates strong foundations for a range of specialist or senior roles. Typical next steps include senior data scientist positions, machine learning engineering, data architecture, or analytical leadership roles. Some graduates go on to postgraduate study or research. Because data science skills apply across sectors, including finance, healthcare, retail and the public sector, there is considerable flexibility in the direction a career can take.
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Curated by Alex Lockey, FATP founder and editor. Last reviewed: .
Sources include the apprenticeship's official specification on apprenticeships.gov.uk, Skills England guidance, IfATE archive records, DWP funding bands, and provider data sourced directly from the public Apprenticeship Provider and Assessment Register (APAR). Standard reference: 337.
Some sections on this page were drafted with AI assistance from published source data and reviewed by a human editor before publication. See our editorial methodology for how we maintain this content. Spotted something out of date? Tell us.
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