UK

United Kingdom · Compliance, quality and safety · Senior (5-8 years)

Senior AI Ethics & Compliance Specialist

Here is the whole job, in plain words. What it is, what you decide, how you're judged, how people get here and where they go next. Then the part no course gives you: twelve AI tutors who learn your work.

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  • Experience bandSenior (5-8 years)
  • Direct reportsNo direct reports
  • Reports toLead AI Governance & Risk Strategist
  • UK framework levelUsually a manager, or the deepest specialist in a team

Also advertised as Senior Responsible AI Specialist · AI Governance Lead · Senior AI Risk Analyst

Built on 43,079 real UK job descriptions we analysed · grounded in qualifications employers recognise

Two short quizzes about you

1From this job to a course

The qualifications matched to this job

NCC Education Level 5 Extended Diploma in Computing with Data Science NCC Education Limited · Level 5 · covers 1 of 4 standards
Qualifi Level 5 Extended Diploma in Computer Science Qualifi Ltd · Level 5 · covers 1 of 4 standards
LRN Level 3 Award in Artificial Intelligence Competence Learning Resource Network · Level 3 · covers 2 of 4 standards
Qualifi Level 4 Diploma in AI Qualifi Ltd · Level 4 · covers 1 of 4 standards
SIAS Level 2 Award in Applying Artificial Intelligence in Business SIAS · Level 2 · covers 1 of 4 standards
NOCN Level 2 Certificate in AI Awareness NOCN · Level 2 · covers 1 of 4 standards

Matched from the standards this job is measured against: a suggestion, not an official ruling.

The units you would start with

Artificial Intelligence NCC Education Level 5 Extended Diploma in Computing with Data Science
Introduction to Artificial Intelligence Qualifi Level 5 Extended Diploma in Computer Science
Artificial Intelligence Project Design & Communication LRN Level 3 Award in Artificial Intelligence Competence
Introduction to Artificial Intelligence and Applications Qualifi Level 4 Diploma in AI
Using Artificial Intelligence in Business SIAS Level 2 Award in Applying Artificial Intelligence in Business
AI and Your Career NOCN Level 2 Certificate in AI Awareness

The future skills this job leans on

AI Fluency working with AI as a thinking partner
AI Governance Fluency using AI responsibly
Systems Fluency seeing the whole picture
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Built for Senior AI Ethics & Compliance Specialist

6 units that map to this job, from the qualifications that cover it.

  1. Artificial Intelligence NCC Education Limited · covers 1 of 4 standards Level 5
  2. Introduction to Artificial Intelligence Qualifi Ltd · covers 1 of 4 standards Level 5
  3. Artificial Intelligence Project Design & Communication Learning Resource Network · covers 2 of 4 standards Level 3
  4. Introduction to Artificial Intelligence and Applications Qualifi Ltd · covers 1 of 4 standards Level 4
  5. Using Artificial Intelligence in Business SIAS · covers 1 of 4 standards Level 2
  6. AI and Your Career NOCN · covers 1 of 4 standards Level 2
These are the real units behind this job, in the order they rank for it. Nothing here is marked done, because this plan has not been started by anyone yet. Yours would fill in as you go.

2AI and this work

Will AI change your work as a Senior AI Ethics & Compliance Specialist?

Nobody can say exactly how. Here is what this job is being asked for next, and where to start.

The new skills this role is being asked for

  • Prompt Engineering & LLM Governance
  • AI Explainability for Regulators
Why each one is rising

The future skills this job leans on

We'll only ever tell you what we can actually back up. No hype, no scare tactics.

What happens when you start

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3What this role really is

This role involves checking that AI systems are built and used responsibly, ethically, and legally. You look for ways to develop AI safely, rather than simply blocking projects. Ethical practice gets built into the development process from the start, not added at the end.

4What you'd actually use

The tools this job runs on, and how well you'd need to know each one.

GRC & Privacy Platforms (e.g., OneTrust, ServiceNow GRC) Advanced

Configuring assessment templates (PIAs, AIAs), designing risk workflows, and building dashboards to track compliance status across multiple AI projects. You'll own the setup for your workstreams.

Data Governance & Catalogue Tools (e.g., Collibra, Alation) Advanced

Defining and enforcing data governance rules for AI/ML datasets. Auditing data lineage to ensure compliance with data minimisation and purpose limitation principles. You'll be the go-to person for data ethics.

AI Model Validation & Monitoring (e.g., Fiddler AI, Arize AI) Expert

Using these tools to conduct deep-dive investigations into model performance degradation or fairness violations. Configuring custom monitors and alert thresholds. You'll be the expert user.

Policy & Procedure Management (e.g., PolicyTech, Confluence) Advanced

Owning and maintaining the central repository of AI policies and standards. Using the platform to manage the full lifecycle of policy review, approval, and publication for your domain.

Executive Reporting & BI (e.g., Power BI, Tableau Server) Advanced

Designing and building interactive dashboards that allow stakeholders to drill down into AI risk metrics, incident trends, and compliance heatmaps. You'll be telling the story with data.

Python Libraries (e.g., pandas, SHAP, Fairlearn) Expert

Writing custom Python scripts to perform sophisticated bias analysis, generate counterfactual explanations, and validate fairness metrics before model deployment. You'll be getting your hands dirty with code.

5What you get to decide, and how that grows

Power in a job isn't your title. It's what you're allowed to decide. Here's how it grows as you move up.

The choiceComing in Where you are nowThe step above
AI Risk Assessment Methodology Executes pre-defined steps within the established methodology, escalates any deviations. Chooses appropriate sub-methods for routine assessments, proposes minor improvements to existing methodology. Designs and implements new or significantly revised AIA methodologies, selects and integrates new tools for assessment. Recommends but doesn't unilaterally approve major framework changes.
Policy Interpretation & Guidance Applies existing policies to straightforward scenarios, flags ambiguous situations for review. Interprets policies for moderately complex cases, drafts initial responses to policy queries, proposes clarifications to existing policies. Provides definitive interpretations for complex policy scenarios, drafts new policy sections or full policies, leads cross-functional discussions to reach consensus on policy application. Consults Legal on legal interpretations.
Bias Mitigation Strategy Runs pre-built bias detection tests and reports findings, suggests standard mitigation techniques from a playbook. Independently identifies bias, proposes and implements standard mitigation techniques, and evaluates their effectiveness. Designs custom bias detection and mitigation strategies for novel AI systems, leads the selection and implementation of advanced fairness tools, and influences product design to embed fairness from the outset. Recommends significant architectural changes.
Project Resource Allocation (within own workstream) No authority, follows assigned tasks. Suggests minor adjustments to personal task prioritisation, informs manager of potential delays. Manages time and resources across multiple assigned workstreams, identifies potential resource conflicts, and proposes solutions. Can recommend spending up to £10K on specialist tools or training for their workstream, subject to Lead approval.

6How you'll be judged

The scoreboard, and what is not on it. The hard targets, how often each is actually looked at, and the quiet signals that never reach a dashboard.

AI Risk Assessment Completion Rate
Percentage of new AI projects or significant model updates that complete their Algorithmic Impact Assessment (AIA) before deployment.
Target · 95% of all relevant projects

In Q3, 28 out of 30 new AI features had a fully approved AIA before launch, hitting 93%. You'd then analyse the two that missed.

Policy & Control Adoption Score
The average score from internal surveys or audits on how well development teams understand and apply our AI ethics policies and controls.
Target · Achieve an average score of 4.0/5.0

After you led the rollout of the new 'Bias Mitigation Playbook', the average score from data scientists on 'clarity of guidance' jumped from 3.2 to 4.1.

Time-to-Approval for Medium-Risk AI Projects
The average number of days it takes for a medium-risk AI project to receive compliance approval, from initial submission to final sign-off.
Target · Reduce by 15% (e.g., from 30 days to 25.5 days)

You streamlined the AIA review process, cutting the average approval time for new recommendation engine features from 28 days to 23 days over six months.

Mentee Progression & Satisfaction
The number of junior team members you've formally mentored who achieve promotion or significantly increase their responsibilities, coupled with their feedback on your guidance.
Target · Two mentees achieve promotion within 24 months; average mentee satisfaction of 4.5/5.0

Both Sarah (L1) and Tom (L2), whom you mentored, were promoted last year, and they both highlighted your support in their development plans.

Clarity & Utility of Guidance
How effectively you translate complex regulatory requirements into clear, actionable advice that engineering and product teams can actually use.
  • Teams proactively seek your input on new AI designs
  • your policy documents are frequently referenced
  • positive feedback in post-project retrospectives regarding your guidance
  • you're often asked to present on complex topics.
Proactive Risk Identification
Your ability to spot potential AI ethics and compliance issues early in the development cycle, before they become costly problems.
  • You flag emerging risks in project kick-offs
  • you propose preventative controls that are adopted
  • fewer 'fire drills' related to AI compliance post-deployment
  • your insights inform strategic discussions on AI roadmap.
Cross-Functional Influence
Your effectiveness in getting different teams (Product, Engineering, Legal) to agree on and adopt responsible AI practices, even when it means slowing down or changing course.
  • You successfully mediate disagreements between teams on ethical considerations
  • your recommendations are consistently adopted by project leads
  • you're invited to early-stage planning meetings for new AI initiatives
  • you're seen as a trusted advisor, not just a blocker.

7Would you like it

Both sides. What people enjoy, and what grinds them down.

What people enjoy
Making a Tangible Impact on Responsible Tech

You'll feel a real sense of purpose when you see a problematic AI feature redesigned because of your input, or when a new policy you helped write prevents a potential bias issue. You're not just moving paper; you're shaping how technology affects people's lives.

You successfully convinced the product team to add a 'human-on-the-loop' override for a critical AI decision, directly reducing the risk of automated harm to customers.

Solving Complex, Evolving Problems

The AI ethics landscape changes constantly. You'll thrive on the intellectual challenge of deconstructing new regulations, understanding novel AI risks, and figuring out how to apply ethical principles to cutting-edge technology. It's never boring, that's for sure.

You spent a week deep-diving into the latest draft of the EU AI Act, then drafted a memo for leadership on how it impacts our next-gen product roadmap, identifying both risks and opportunities.

Building and Improving Systems

You love taking a messy problem and bringing order to it. This means designing new assessment templates, streamlining review processes, or creating clear guidelines that make everyone's job easier and more compliant. You're a builder of frameworks and processes.

You led the implementation of a new AI Impact Assessment template in our GRC platform, which reduced the time it took for data scientists to complete their initial risk screens by 30%.

What frustrates people
  • Being brought in too late: You'll often be asked to 'review' an AI model that's 90% built, making it much harder to implement changes without significant delays or rework.
  • The 'Innovation Blocker' label: Constantly battling the perception that your job is to slow things down and say 'no', instead of enabling responsible and sustainable innovation.
  • Data Scientist Apathy: Dealing with brilliant data scientists who view ethics and compliance as bureaucratic hurdles to be 'managed away' rather than a fundamental part of their craft.
  • The Black Box Stand-off: Having to explain to auditors or regulators that, for a complex deep learning model, you can't provide a simple, deterministic reason for a specific output, which flies in the face of traditional compliance expectations.
  • Budgeting for a 'Non-Event': Making the business case to spend millions on AI governance is difficult because success is measured by the fines you didn't get and the scandals that didn't happen.
What this role does not give you
  • A purely technical coding role – while you'll understand the tech, you won't be writing production-level code daily.
  • A static, predictable environment – the regulatory and technological landscape for AI is constantly shifting.
  • A role where all your recommendations are immediately and enthusiastically adopted without discussion or pushback.

8Who you work with

This role directly drives the operational effectiveness of our AI governance framework. Your work ensures that AI systems are developed and deployed in a manner that minimises legal, ethical, and reputational risks, protecting the organisation from significant financial penalties and maintaining public trust. You'll directly shape how our AI products are perceived and received in the market.

Inside the business
  • Product Managers (AI-focused)
  • Data Science & Engineering Leads
  • Legal & Privacy Counsel
  • Internal Audit Team
  • Risk Management Committee
Outside the business
  • External auditors and assurance providers
  • Regulatory bodies (e.g., ICO, FCA, potentially EU AI Board)
  • Key technology vendors
  • Senior clients (especially those with strict compliance needs)

9What you need before you start

Not a wish list. The things you would be expected to already have.

  • At least 5 years of experience in AI ethics, data privacy, compliance, risk management, or a related field, with a clear focus on AI systems.
  • Proven experience leading projects or workstreams, ideally in a cross-functional setting.
  • Demonstrable experience in conducting Algorithmic Impact Assessments (AIAs) or similar risk assessments for AI/ML models.
  • Strong understanding of statistical concepts related to bias detection and fairness metrics in machine learning.
  • Practical experience with at least one GRC or policy management platform.
  • Ability to write clear, concise policy documents and technical guidance for diverse audiences.

10What to practise next

Where the job is going, and what to do about it starting this week.

Automated Compliance Testing (ACT)

Manual compliance checks won't scale with the explosion of AI models. You'll need to understand how to build and integrate automated tests into the CI/CD pipeline for AI, ensuring continuous compliance monitoring.

Compliance-as-Code principles · Integration with MLOps pipelines · Synthetic data generation for testing · Continuous monitoring for drift and bias

  • This month: Research existing open-source libraries for automated AI compliance testing (e.g., IBM AI Fairness 360, Google Responsible AI Toolkit).
  • Next quarter: Collaborate with an MLOps engineer to identify a pilot project for embedding automated compliance checks.
  • Within 6 months: Design a proof-of-concept for an automated fairness test integrated into a model's CI/CD pipeline.
  • Within 9 months: Document the benefits and challenges of ACT for our organisation and propose a rollout plan.

Quick win: Identify one simple, repeatable bias check that could be automated for a low-risk model and work with a data scientist to script it. Small wins build momentum.

11Staying current once you are in

What people here do to keep up
  • Regularly engage with leading AI ethics research papers and industry reports (e.g., from AI Now Institute, Alan Turing Institute, Google AI).
  • Participate in industry forums, webinars, and conferences focused on AI governance, ethics, and responsible AI.
  • Contribute to open-source projects or community initiatives related to AI fairness or explainability.
  • Seek out mentorship from experienced professionals in the AI ethics and compliance space.
  • Take advanced online courses on specific technical aspects of AI (e.g., deep learning, reinforcement learning) to deepen your understanding of the underlying technology.

12The rising capability

The rising capability

Zavmo analysis

What's rising in its place

This is where the work is heading, and the higher pay with it. Get fluent here and the shift stops being a threat and starts being your edge.

Prompt Engineering & LLM Governance

Large Language Models (LLMs) are everywhere now, and they bring entirely new ethical and compliance challenges. It's not just about the model itself, but how people interact with it. Understanding how to 'prompt' these models effectively and govern their use is becoming paramount.

  • Context windows and token limits
  • Hallucination detection and mitigation
  • RAG (Retrieval Augmented Generation) architectures
  • Prompt injection attacks
  • Ethical prompt design

AI Explainability for Regulators

Regulators are increasingly demanding clear, understandable explanations for AI decisions, especially for high-risk systems. It's no longer enough to just detect bias; you need to be able to articulate why a model made a particular decision in a way that satisfies legal and ethical scrutiny.

  • Local vs. Global explanations
  • SHAP and LIME in regulatory contexts
  • Counterfactual explanations for 'right to explanation'
  • Model-agnostic vs. Model-specific interpretability
  • Human-centred explainability

13What you’ll use

What you’ll use

Skills this role draws on

Technical

  • Regulatory Framework Analysis
  • Algorithmic Impact Assessments (AIAs)
  • Bias & Fairness Auditing
  • Explainable AI (XAI) Methods
  • AI Incident Root Cause Analysis (RCA)
  • Risk Management Integration (Three Lines of Defence)

14The pathway

The pathway

How you actually get there, here

How you become one varies far more by country than what one does. This is the UK route. Most people take one of these ways in; the right one depends on where you're starting from.

  1. 1

    Mid-Level AI Ethics & Compliance Specialist (L2)

    2-3 years

    Skills to master

    • Independently owning and completing AI compliance projects, identifying issues and proposing solutions, and starting to informally guide junior colleagues. You'd need to master the end-to-end AIA process for routine models.

    You're ready to move on when

    • Consistently delivers high-quality AI risk assessments with minimal supervision.
    • Proactively identifies and flags potential compliance issues early in the project lifecycle.
    • Receives positive feedback from cross-functional peers on collaboration and advice.
    • Has successfully mentored at least one junior analyst on a complex task.
  2. 2

    Data Scientist / ML Engineer with Ethics Focus

    3-5 years (after initial DS/MLE experience)

    Skills to master

    • Deepening understanding of regulatory frameworks, translating technical bias detection into actionable compliance controls, and developing strong communication skills for non-technical audiences. You'd need to shift from purely technical problem-solving to a more holistic risk management view.

    You're ready to move on when

    • Has actively contributed to responsible AI initiatives within their engineering team.
    • Demonstrates a strong understanding of ethical AI principles beyond just technical fairness metrics.
    • Has taken additional courses or certifications in AI ethics or governance.
    • Can clearly articulate the societal impact of AI systems.
  3. 3

    Compliance Analyst / Risk Manager (Specialising in Tech/AI)

    3-4 years (after initial compliance experience)

    Skills to master

    • Gaining a deeper technical understanding of AI/ML concepts, learning specific AI model validation and monitoring tools, and understanding the nuances of AI-specific regulatory frameworks. You'd need to move beyond general compliance to the specifics of AI.

    You're ready to move on when

    • Has successfully managed compliance for complex technology projects.
    • Demonstrates a strong interest and foundational knowledge in AI/ML.
    • Has experience with risk assessment methodologies and control design.
    • Proactively seeks out opportunities to learn about emerging AI regulations.

15Where this role leads

The long view: This work can lead to managing a compliance programme, directing AI governance for a business, or setting AI ethics strategy at the top of an organisation.

The ten Future Fluencies

Zavmo analysis

The credential is what you can do today. These are what keep you valuable.

A qualification proves you can do the job as it's defined today. These ten are what decide whether you're still the obvious person for it in five years. They're the capabilities employers are now writing into senior roles faster than people are learning them. Zavmo weaves them through whatever you study, so you come out with both: the credential and the fluency.

The highlighted ones are the Fluencies your role leans on hardest, from how Senior AI Ethics & Compliance Specialist is actually changing.

Where it leads

Roles this leads to

Also named as a next step, with no page in the catalogue yet:

  • Principal AI Ethics Advisor (Individual Contributor Path)

How you would learn this

One to one, on the work you already do

A tutor sets the task against your own job and marks the reasoning, at your pace. There is no cohort to fall behind.

A lesson from Artificial Intelligence, worked against Senior AI Ethics & Compliance Specialist.

Artificial Intelligence Level 5

Applied to your work in Senior AI Ethics & Compliance Specialist

This unit aims to provide learners with an understanding of Artificial Intelligence (AI) and its applications, enabling them to apply AI search strategies and knowledge representation techniques to solve problems. Learners will also assess techniques for reasoning with uncertain knowledge and understand machine learning techniques, demonstrating a comprehensive knowledge of AI principles and applications.

How the thinking builds
  1. 1 Remember
  2. 2 Understand
  3. 3 Apply
  4. 4 Analyse
  5. 5 Evaluate
  6. 6 Create
An illustration of a Zavmo lesson, built from this role’s own route. The unit, its objective and every criterion above are the awarding body’s own words, not an example.

It is £70 a month, billed monthly. Your free first module is Future Fluency, and you can cancel any time.

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See Your Progress Grow Illustration
Senior AI Ethics & Compliance Specialist
  • Regulatory Framework Analysis
  • Algorithmic Impact Assessments (AIAs)
  • Bias & Fairness Auditing
  • Explainable AI (XAI) Methods
  • AI Incident Root Cause Analysis (RCA)
  • Risk Management Integration (Three Lines of Defence)
This is your Mind Palace on learn.zavmo.ai. Every skill above comes from this role's own record, not an example borrowed from another job. A node lights up when you evidence it, and what you build stays yours between jobs. That is the part a course cannot do.

16The detail, folded away

Everything else the record holds

The career branches in full, how AI is already showing up in the day-to-day, and the questions people ask about this job. Here when you want them, out of the way while you decide.

Where it leads next, rung by rung

Where it leads

The career path, and where it branches

Senior AI Ethics & Compliance Specialist is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

  1. Lead AI Governance & Risk Strategist (L4)

    3-5 years

    You'd move from leading workstreams to architecting major components of the entire AI governance framework. This means defining strategy, building new capabilities, and potentially managing a small team.

    • Enterprise AI Risk Framework Development Designing the entire AI risk framework, including risk taxonomies and appetite statements.
    • Vendor Risk Management (AI-specific) Establishing processes for assessing and managing the ethical and compliance risks of third-party AI solutions.
    • Regulatory Foresight Proactively anticipating future regulatory changes and developing strategies to prepare the organisation.

  2. Principal AI Ethics Advisor (Individual Contributor Path)

    3-5 years

    This path focuses on becoming a deep subject matter expert, providing advanced technical and ethical guidance without direct people management responsibilities. You'd be the 'go-to' expert for the trickiest problems.

    • Novel AI Risk Modelling Developing new methods for assessing risks associated with emerging AI paradigms (e.g., AGI, foundation models).
    • Advanced XAI & Fairness Research Pushing the boundaries of how we explain and ensure fairness in highly complex models.
    • External Engagement Representing the company in industry working groups or standards bodies.
Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, the sheer volume of regulations and the pace of AI development can feel overwhelming. But here's the thing: you don't have to do it all manually. We're embracing AI ourselves to make your job as a Senior AI Ethics & Compliance Specialist more efficient, more impactful, and frankly, less tedious.

Imagine having a digital assistant that handles the grunt work, freeing you up to focus on the truly strategic and complex ethical challenges. Our AI Productivity Hub is designed to do just that, giving you superpowers in regulatory analysis, control mapping, and policy drafting. It's about working smarter, not just harder.

Regulatory Radar Automation

Use an AI agent to continuously scan global regulatory bodies, legal journals, and government publications for updates related to AI. The AI summarises changes, flags conflicts with existing internal policies, and even auto-generates a draft impact assessment for your review. No more endless legal website trawling.

Control Mapping Accelerator

When a new regulation (like the EU AI Act) is finalised, use an LLM to analyse the text and automatically map its requirements against our company's existing library of controls in the GRC platform. It instantly highlights gaps where new controls are needed, saving you weeks of painstaking manual cross-referencing.

Institutional Knowledge Chatbot

Train a private LLM on all internal AI policies, past risk assessments, and incident reports. This allows developers to self-serve answers to common questions ('What are the rules for using health data in a model?') instantly, drastically reducing the number of routine queries that land in your inbox.

First-Draft Policy Generation

Use a generative AI tool to create a first draft of a new policy (e.g., 'Policy on Synthetic Data Generation'). You provide a prompt with key principles and requirements, and the AI generates a structured, well-formatted document that you can then refine and edit. Say goodbye to 'blank page' syndrome.

Common questions

Common questions

How do you become a Senior AI Ethics & Compliance Specialist?

Common routes in include Mid-Level AI Ethics & Compliance Specialist (2-3 years), Data Scientist / ML Engineer with Ethics Focus (3-5 years (after initial DS/MLE experience)) and Compliance Analyst / Risk Manager (Specialising in Tech/AI) (3-4 years (after initial compliance experience)). Times vary with prior experience.

Where can a Senior AI Ethics & Compliance Specialist progress to?

This role can lead on to Lead AI Governance & Risk Strategist (3-5 years) and Principal AI Ethics Advisor (Individual Contributor Path) (3-5 years), depending on the skills you build.

What new skills matter most for a Senior AI Ethics & Compliance Specialist?

Increasingly, Prompt Engineering & LLM Governance and AI Explainability for Regulators. These are the skills our record of this role lists as growing.

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17Where to go from here

Other roles at Level 5

Same depth of qualification, different job. Useful if the work appeals but this particular role does not.

 More jobs in compliance, quality and safety

Stay in the field you know and move sideways rather than up.
If you leave this industry

The skills involved carry across sectors. They can lead to an AI ethics role at a large tech firm. They can also lead to work as a responsible AI consultant, or a move into policy development for a government body or think tank.

This role profile is © 2026 Growth Engineering Technologies Ltd. Built from UK occupational standards and regulated qualification data, and written for Zavmo.

You're not behind. You're right on time. The shift is only just beginning. Your role won't look the same in two years. Be the one who leads the change, not the one it happens to. Start my plan, free What happens next: a free account, then we build your plan as you set up. No card. Build your plan, see your roadmap and meet the twelve tutors matched to you. All free. When you're ready to start learning, it's £70 a month, billed monthly. Cancel any time and billing stops.