UK

United Kingdom · Technology and data · Director/VP (16-20 years)

Director, Computational Biology

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  • Experience bandDirector/VP (16-20 years)
  • Direct reports25-100+ reports
  • Reports toVP, Computational Science
  • UK framework levelUsually a director, accountable for a division and its numbers

Also advertised as VP of Bioinformatics · Head of 'Omics Data Science · Director of Genomics Platforms

Built on 43,079 real UK job descriptions we analysed · grounded in what real qualifications cover

Two short quizzes about you

1From this job to a course

The qualifications matched to this job

OTHM Level 7 Diploma in Data Science OTHM Qualifications · Level 7 · covers 5 of 16 standards
Pearson BTEC Level 5 Higher National Diploma in Computing for England Pearson Education Ltd · Level 5 · covers 8 of 16 standards
Pearson BTEC Level 5 Higher National Diploma in Digital Technologies Pearson Education Ltd · Level 5 · covers 3 of 16 standards
Qualifi Level 5 Diploma in AI Qualifi Ltd · Level 5 · covers 2 of 16 standards
NCFE Level 5 Diploma: Data Engineer NCFE · Level 5 · covers 1 of 16 standards
NOCN Level 4 certificate for Data Analysts NOCN · Level 4 · covers 7 of 16 standards

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

The units you would start with

Data Analysis and Visualisation OTHM Level 7 Diploma in Data Science
Advanced Programming for Data Analysis Pearson BTEC Level 5 Higher National Diploma in Computing for England
Data Analytics Pearson BTEC Level 5 Higher National Diploma in Digital Technologies
Visualisation Qualifi Level 5 Diploma in AI
Data pipelines and automation NCFE Level 5 Diploma: Data Engineer
Data Analytics Primer NOCN Level 4 certificate for Data Analysts

The future skills this job leans on

AI Fluency working with AI as a thinking partner
Data Fluency turning numbers into a decision
AI Governance Fluency using AI responsibly
Frontier Fluency the next wave, beyond mainstream AI
Systems Fluency seeing the whole picture
Your Plan Illustration

Built for Director, Computational Biology

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

  1. Data Analysis and Visualisation OTHM Qualifications · covers 5 of 16 standards Level 7
  2. Advanced Programming for Data Analysis Pearson Education Ltd · covers 8 of 16 standards Level 5
  3. Data Analytics Pearson Education Ltd · covers 3 of 16 standards Level 5
  4. Visualisation Qualifi Ltd · covers 2 of 16 standards Level 5
  5. Data pipelines and automation NCFE · covers 1 of 16 standards Level 5
  6. Data Analytics Primer NOCN · covers 7 of 16 standards Level 4
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 Director, Computational Biology?

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

  • Ethical AI & Data Governance in Genomics
  • Quantum Computing for 'Omics' Strategic Evaluation
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

Next, a free account. We build your Director, Computational Biology plan as you set up, starting with the free Data Fluency module. No card.

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Keep this read. One email with what AI is changing in this job, the new skills it asks for and where to start free. Nothing else follows unless you confirm your address.

3What this role really is

This role shapes the strategic direction of a computational biology function, setting priorities and managing budgets across multiple years.

4What you'd actually use

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

Strategic Language/Platform Choice (Python, R, Julia) Expert

Setting coding standards for the department, evaluating and deciding on the adoption of new programming languages or frameworks for specific high-performance or strategic needs. You're making the calls on our tech stack's future.

Sequencing Tools Architecture (GATK, BWA, STAR) Expert

Evaluating and benchmarking new bioinformatics algorithms and tools for enterprise-wide adoption. Making build-vs-buy decisions on core analytical platforms and ensuring they integrate with our existing infrastructure.

Workflow Management Strategy (Nextflow, Snakemake) Expert

Architecting the entire computational ecosystem, ensuring workflow management systems integrate seamlessly with HPC/cloud schedulers, data storage, and LIMS. You're designing how all our pipelines will run efficiently at scale.

Cloud & HPC Architecture (AWS, Azure, GCP, SLURM) Expert

Designing and implementing the organisation's cloud bioinformatics strategy (e.g., building serverless analysis pipelines with AWS Step Functions/Lambda). Managing budgets and optimising for cost/performance trade-offs across all computational resources.

Enterprise Data Visualisation & Reporting (Tableau, Spotfire, Power BI) Expert

Selecting and implementing enterprise-level visualisation platforms for integrating 'omics' data with clinical or business intelligence data. Ensuring our scientific insights are accessible and impactful for all stakeholders, including the C-suite.

Version Control Administration & CI/CD (GitHub, GitLab, GitHub Actions) Expert

Establishing and managing the organisation's version control infrastructure and policies, including CI/CD pipelines for bioinformatics tools. Ensuring code quality, reproducibility, and efficient deployment across all teams.

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
Strategic Roadmap & Investment N/A N/A Defines and owns the 3-5 year strategic roadmap for computational biology, including major platform investments (e.g., £2M+ cloud infrastructure). Consults VP/CSO for final approval, but drives the recommendation.
Budget Allocation & Management N/A N/A Full P&L responsibility for the computational biology department (£2M-£10M+). Allocates funds across teams, projects, and technologies. Approves expenditures up to £500K without further VP approval.
Team Structure & Hiring N/A N/A Defines organisational structure for the department. Full authority for hiring, performance management, and career progression for all direct reports (managers and principal scientists). Oversees hiring for the entire department.
Technology & Platform Adoption N/A N/A Evaluates, selects, and drives the adoption of enterprise-level bioinformatics platforms and key technologies. Makes build-vs-buy decisions for core capabilities. Consults IT for integration.

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.

ROI on Bioinformatics Platform Investments
The return on investment for major bioinformatics platform upgrades or new technology adoptions.
Target · Achieve >15% ROI on major platform investments within 18 months.

After investing £2M in a new cloud-based 'omics' analysis platform, the team's throughput increased by 30%, and operational costs decreased by £350K annually, yielding a strong ROI.

Cloud Computing Cost Optimisation
Reducing the overall spend on cloud resources for 'omics' data storage and analysis, without compromising performance or security.
Target · Reduce annual cloud spend by 10-15% year-on-year.

Through strategic resource management and optimising pipeline architectures, you cut our AWS bill by £100K in Q2 while maintaining or improving analysis turnaround times.

Novel Drug Target Identification Rate
The number of computationally identified novel drug targets that progress to the next stage gate in our R&D pipeline.
Target · Contribute to the identification of 3-5 novel, validated drug targets annually.

Your team's analysis of patient genomic data led to the identification of four promising new targets, two of which are now in preclinical validation.

Data Quality & Reproducibility Score
An aggregated score reflecting the quality of raw data processed and the reproducibility of analysis results across the department.
Target · Maintain a data quality score of >90% and a reproducibility index of >95% for all critical pipelines.

A recent audit showed that 98% of all critical analysis outputs could be fully reproduced by an independent team member, and incoming data quality issues were flagged and resolved proactively.

Strategic Influence & Thought Leadership
Your ability to influence the overall scientific strategy of the organisation and establish our external reputation in computational biology.
  • Regularly invited to present at C-suite strategy sessions
  • sought out by R&D heads for advice on new scientific directions
  • publications in top-tier journals
  • speaking engagements at major conferences
  • active participation in industry working groups.
Talent Development & Retention
How effectively you build, mentor, and retain a high-performing team, fostering a culture of continuous learning and growth.
  • High team engagement scores (e.g., >80%)
  • low voluntary attrition rates (<10%)
  • clear career pathways for team members
  • successful promotions from within your department
  • positive feedback from direct reports and skip-level reports on mentorship and leadership.
Cross-Functional Collaboration & Partnership
Your effectiveness in building strong working relationships and strategic partnerships with other departments (e.g., wet-lab R&D, Clinical, IT) and external collaborators.
  • Successful joint projects with other departments
  • positive feedback from internal and external partners on collaboration effectiveness
  • active participation in cross-functional steering committees
  • successful negotiation of complex inter-departmental resource allocations.

7Would you like it

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

What people enjoy
Shaping Scientific Strategy

You'll spend a good chunk of your week in strategic planning meetings, reviewing scientific literature, and brainstorming with R&D heads to identify the next big questions our computational biology team can answer. This means translating high-level scientific goals into concrete technical roadmaps and resource plans.

Leading the initiative to integrate spatial transcriptomics data into our drug discovery platform, defining how it will impact target validation and patient stratification over the next three years.

Building High-Performing Teams

You'll be heavily involved in talent acquisition, performance reviews, and career development discussions for your direct reports and their teams. This means creating a culture where top computational biologists thrive, learn, and feel empowered to innovate. You'll spend time mentoring, coaching, and unblocking your managers.

Successfully recruiting a new Head of Genomics Data Analysis, then working closely with them to build out their sub-team and define their first-year objectives.

Driving Impact at Scale

Your decisions will impact hundreds of projects and potentially millions of pounds in R&D investment. You'll be motivated by seeing your team's work accelerate drug discovery, improve clinical outcomes, or lead to significant scientific publications. This means focusing on the big picture and ensuring every computational effort contributes to a larger goal.

Overseeing the deployment of a new enterprise-wide bioinformatics platform that reduces analysis turnaround time for all R&D teams by 25%, directly impacting project timelines.

What frustrates people
  • Bureaucracy and organisational inertia that slow down strategic initiatives.
  • Budget constraints that force difficult trade-offs between innovation and operational stability.
  • Resistance to change from other departments who prefer 'the old way' of doing things.
  • The constant need to justify investment in long-term platform development over immediate, short-term research requests.
  • Attracting and retaining top-tier computational biology talent in a highly competitive market.
What this role does not give you
  • A daily deep dive into writing complex Python or R code (you'll oversee it, not write it).
  • Complete autonomy without any organisational constraints or political considerations.
  • A predictable, unchanging work environment; strategic priorities shift, and you'll need to adapt.
  • The ability to avoid difficult conversations about performance, budget, or strategic direction.

8Who you work with

This role directly influences the success of our R&D pipeline, from early-stage target identification to late-stage clinical trial analysis. You'll be accountable for the quality, speed, and strategic relevance of all 'omics' data insights, which ultimately impacts our product portfolio and market position. Your decisions will shape our scientific direction for years to come, affecting millions of pounds in investment and potentially patient outcomes.

Inside the business
  • C-Suite (CEO, CSO)
  • Heads of R&D
  • Clinical Development Leads
  • Data Science & AI Leadership
  • IT & Infrastructure Teams
Outside the business
  • Academic Research Partners
  • Key Technology Vendors (e.g., cloud providers, sequencing tech)
  • Regulatory Bodies (e.g., MHRA, EMA)
  • Investors and Board Members

9What you need before you start

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

  • A proven track record of at least 5-7 years in a senior leadership role (e.g., Principal Scientist, Head of Bioinformatics) managing large teams (20+ people) and significant budgets (£2M+) in a relevant scientific or technical domain.
  • Demonstrated ability to define and execute complex scientific strategies that have led to tangible business or research outcomes.
  • Extensive experience in architecting and deploying enterprise-scale bioinformatics platforms and data analysis solutions.
  • A strong publication record in top-tier scientific journals and a history of presenting at major international conferences, showcasing thought leadership.
  • Experience in managing and developing a diverse team of highly skilled scientists and engineers, fostering a culture of innovation and continuous improvement.

10What to practise next

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

Advanced Spatial 'Omics' & Multi-Modal Data Integration

New technologies are providing unprecedented cellular and spatial resolution, generating incredibly rich, multi-modal datasets. The challenge (and opportunity) is integrating these complex data types to gain a holistic understanding of disease biology.

Spatial Transcriptomics Analysis · Single-Cell Multi-Omics Integration · Graph Neural Networks for Biological Networks · Data Fusion & Harmonisation Strategies

  • This quarter: Review the latest publications on spatial 'omics' and multi-modal data integration.
  • Next 6 months: Identify a pilot project within R&D that could benefit from integrating these advanced data types.
  • Next 12 months: Ensure your team has the necessary training and computational infrastructure to support these analyses.
  • Ongoing: Foster collaborations with wet-lab teams generating these complex datasets.

Quick win: Have your team present a 'state-of-the-art' review on spatial 'omics' to the wider R&D group to spark interest and identify potential applications.

11Staying current once you are in

What people here do to keep up
  • Regularly attend and present at major international conferences (e.g., ASHG, AACR, ECCB) to stay abreast of scientific advancements and maintain a strong professional network.
  • Actively participate in industry consortia or working groups focused on bioinformatics standards, data sharing, or ethical AI in genomics.
  • Engage in continuous learning through executive education programmes, online courses, or self-study on emerging technologies (e.g., quantum computing, advanced AI/ML).
  • Mentor junior and mid-career scientists, both within and outside your organisation, to foster the next generation of talent.

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.

Ethical AI & Data Governance in Genomics

With the increasing use of AI in drug discovery and clinical genomics, regulatory bodies and the public are demanding greater transparency, fairness, and privacy. Ignoring this isn't an option; it's a strategic imperative to build trust and ensure compliance.

  • AI Fairness & Bias Mitigation
  • Explainable AI (XAI) for Clinical Decisions
  • Privacy-Preserving AI Techniques
  • Regulatory Frameworks for AI in Healthcare

Quantum Computing for 'Omics' Strategic Evaluation

While still early, quantum computing holds the potential to revolutionise complex simulations and data analyses that are currently intractable for classical computers. As a Director, you need to be evaluating its strategic relevance and preparing for its eventual impact, even if it's years away.

  • Quantum Algorithms for Molecular Simulation
  • Quantum Machine Learning (QML)
  • Hybrid Classical-Quantum Approaches
  • Quantum Hardware Landscape

13What you’ll use

What you’ll use

Skills this role draws on

Technical

  • Next-Generation Sequencing (NGS) Data Analysis (Strategic Oversight)
  • Statistical Genetics & Genomics (Advanced Application Guidance)
  • Reproducible Pipeline Development (Organisational Standard Setting)
  • Biological Interpretation & Pathway Analysis (Strategic Insight Generation)
  • 'Omics' Data QC & Wrangling (Enterprise-Level Strategy)

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

    Principal Scientist / Head of Bioinformatics (from another organisation)

    You'd typically spend 3-5 years in a Principal or Head role, demonstrating your ability to lead significant scientific programmes and manage smaller teams.

    Skills to master

    • Strategic programme leadership, cross-functional influence, initial budget management, and talent development for a specific domain.

    You're ready to move on when

    • Successfully led 2-3 major, multi-year scientific programmes from conception to delivery.
    • Managed a team of 10-20 scientists and engineers, with demonstrable impact on their career growth.
    • Influenced scientific strategy at a departmental or functional level.
    • Presented at major international conferences and published in peer-reviewed journals.
  2. 2

    Senior Manager of Computational Biology (internal promotion)

    An internal candidate would usually spend 4-6 years as a Senior Manager, demonstrating the ability to manage multiple teams and complex projects.

    Skills to master

    • Scaling team operations, managing a larger budget, navigating organisational politics, and developing a broader strategic perspective.

    You're ready to move on when

    • Consistently exceeded performance expectations as a Senior Manager, with strong feedback from direct reports and peers.
    • Successfully managed a budget of £1M+ and demonstrated financial acumen.
    • Led cross-functional initiatives that delivered significant value to the organisation.
    • Actively mentored other managers and contributed to departmental strategy.

15Where this role leads

The long view: This work can lead to roles such as Chief Technology Officer or Chief Executive Officer of a biotech startup, or a position as a board member or scientific advisor.

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 Director, Computational Biology is actually changing.

Where it leads

Roles this leads to

The record names a next step, but the catalogue has no page for it yet:

  • VP, Computational Science
  • Chief Scientific Officer (CSO) / Head of R&D (in a smaller or mid-sized biotech)

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 Data Analysis and Visualisation, worked against Director, Computational Biology.

Data Analysis and Visualisation Level 7

Applied to your work in Director, Computational Biology

1. To enable the learner to critically analyse the theoretical underpinnings of data analytics and their impact on decision-making in business management contexts. 2. To enable the learner to assess diverse data analysis activities, techniques, and tools applicable to business management scenarios. 3. To enable the learner to compare and contrast various predictive analytic techniques, evaluating their strengths and weaknesses in forecasting future business events. 4. To enable the learner to evaluate how predictive analytic techniques can be practically implemented for forecasting purposes within the business sector. 5. To enable the learner to evaluate prescriptive analytic techniques, illustrating their application with relevant examples from the business management domain. 6. To enable the learner to apply a suitable programming language or data analysis tool to conduct data analysis and visualisation tasks related to business management problems.

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.

See Your Progress Grow Illustration
Director, Computational Biology
  • Next-Generation Sequencing (NGS) Data Analysis (Strategic Oversight)
  • Statistical Genetics & Genomics (Advanced Application Guidance)
  • Reproducible Pipeline Development (Organisational Standard Setting)
  • Biological Interpretation & Pathway Analysis (Strategic Insight Generation)
  • 'Omics' Data QC & Wrangling (Enterprise-Level Strategy)
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

Director, Computational Biology is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

  1. VP, Computational Science

    Roughly 3-5 years in the Director role, demonstrating consistent strategic impact and leadership at an enterprise level.

    This is a significant jump, moving from leading a department to overseeing an entire functional area, often with broader scope and direct C-suite interaction.

    • Broader scientific domain knowledge beyond 'omics' (e.g., structural biology, chemistry).
    • Advanced P&L management for a larger business unit.
    • Global regulatory affairs and market access strategies.
    • Strategic external partnership development at a corporate level.

  2. Chief Scientific Officer (CSO) / Head of R&D (in a smaller or mid-sized biotech)

    Could be 4-7 years in the Director role, often requiring a move to an organisation where the CSO role has a strong computational focus.

    This involves taking on ultimate accountability for the entire scientific strategy and R&D pipeline of a company, often with direct board reporting.

    • Clinical development strategy and regulatory interactions.
    • Portfolio management and resource allocation across all scientific disciplines.
    • Scientific communications and public relations at a corporate level.
    • Ultimate scientific governance and ethical oversight.
Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, at Director level, your time is gold. You're not just looking for marginal gains; you're looking for step-changes in productivity and strategic insight. AI isn't just a buzzword; it's a powerful enabler for your entire computational biology function, freeing up your team from the mundane so they can focus on the truly groundbreaking science.

By strategically integrating AI tools, you can supercharge your team's output, accelerate scientific discovery, and give your leaders more headspace for the big, hairy problems. We're talking about moving from 'how do we do this?' to 'what should we be doing next?' It's about amplifying human intelligence, not replacing it, and ensuring your department is at the forefront of AI-driven research.

Strategic Pipeline Automation

Imagine AI helping your team design and optimise entire analysis workflows, not just individual scripts. This means AI assisting in selecting the best tools, suggesting parameter optimisations, and even predicting potential failure points before a single run. It's about moving from manual pipeline construction to AI-assisted architectural design, ensuring robustness and scalability across the department.

Accelerated Scientific Discovery

Your team spends hours sifting through literature or trying to connect disparate biological findings. AI can rapidly summarise vast amounts of scientific papers, identify novel hypotheses from complex 'omics' data, or even predict potential drug targets based on integrated biological networks. This frees up your scientists to focus on experimental validation and deeper interpretation, rather than information retrieval.

Organisational Debugging & Root Cause Analysis

When a major analysis pipeline fails or a data quality issue emerges across multiple projects, AI can help identify systemic problems much faster. Instead of individual analysts spending days tracing errors, an LLM can analyse logs, configuration files, and historical data to pinpoint the root cause, whether it's a dependency conflict, a cloud resource issue, or a subtle batch effect. This means less downtime and more reliable results for the entire department.

Executive Communication Drafting

You're constantly drafting strategic reports, board presentations, investor updates, and internal communications. AI can assist in generating first drafts of these documents, summarising complex scientific findings into clear, concise language suitable for executive audiences. This saves you and your leadership team significant time, allowing you to focus on refining the message and strategic implications, rather than staring at a blank page.

Common questions

Common questions

How do you become a Director, Computational Biology?

Common routes in include Principal Scientist / Head of Bioinformatics (from another organisation) (You'd typically spend 3-5 years in a Principal or Head role, demonstrating your ability to lead significant scientific programmes and manage smaller teams.) and Senior Manager of Computational Biology (internal promotion) (An internal candidate would usually spend 4-6 years as a Senior Manager, demonstrating the ability to manage multiple teams and complex projects.). Times vary with prior experience.

Where can a Director, Computational Biology progress to?

This role can lead on to VP, Computational Science (Roughly 3-5 years in the Director role, demonstrating consistent strategic impact and leadership at an enterprise level.) and Chief Scientific Officer (CSO) / Head of R&D (in a smaller or mid-sized biotech) (Could be 4-7 years in the Director role, often requiring a move to an organisation where the CSO role has a strong computational focus.), depending on the skills you build.

What new skills matter most for a Director, Computational Biology?

Increasingly, Ethical AI & Data Governance in Genomics and Quantum Computing for 'Omics' Strategic Evaluation. 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 7

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

 More jobs in technology and data

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

Skills built in this role carry across biotech, pharmaceutical and healthcare technology sectors. Paths can lead into genomics startups, large pharma R&D, clinical diagnostics companies, or venture capital firms focused on life sciences. Turning complex scientific data into strategic decisions is valued in each of these settings.

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 free Data Fluency module 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.