UK

United Kingdom · Research and development · Lead (8-12 years)

Principal Scientist / Research Lead

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 bandLead (8-12 years)
  • Direct reports3-8 reports
  • Reports toResearch Manager / Group Leader
  • UK framework levelUsually a manager, or the deepest specialist in a team

Also advertised as Lead Research Scientist · Senior Principal Scientist · Research Programme Lead · Technical Lead, R&D

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

NCC Education Level 5 Extended Diploma in Computing with Data Science NCC Education Limited · Level 5 · covers 1 of 3 standards
Level 6 Award in Data-led Decision Making Institute of Sales Professionals · Level 6 · covers 1 of 3 standards
Level 6 Certificate in Professional Sales Institute of Sales Professionals · Level 6 · covers 1 of 3 standards
Level 6 Diploma in Professional Sales Institute of Sales Professionals · Level 6 · covers 1 of 3 standards
TQUK Level 6 Diploma in Digital Leadership (RQF) Training Qualifications UK Ltd · Level 6 · covers 1 of 3 standards

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

The units you would start with

Introduction to Data Science and Big Data NCC Education Level 5 Extended Diploma in Computing with Data Science
Data-led Decision Making Level 6 Award in Data-led Decision Making
Apply the Concepts of Data Science to Computer Engineering NOCN Level 5 Diploma in Computer Engineering (Information Technology and Data Science)
Data science TQUK Level 6 Diploma in Digital Leadership (RQF)

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
Automation Fluency building AI into how work runs
Systems Fluency seeing the whole picture
Your Plan Illustration

Built for Principal Scientist / Research Lead

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

  1. Introduction to Data Science and Big Data NCC Education Limited · covers 1 of 3 standards Level 5
  2. Data-led Decision Making Institute of Sales Professionals · covers 1 of 3 standards Level 6
  3. Apply the Concepts of Data Science to Computer Engineering NOCN · covers 1 of 3 standards Level 5
  4. Data science Training Qualifications UK Ltd · covers 1 of 3 standards Level 6
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 Principal Scientist / Research Lead?

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 & Advanced LLM Integration for Scientific Discovery
  • Advanced Data Visualisation for Strategic Storytelling
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 Principal Scientist / Research Lead plan as you set up, starting with the free Automation Fluency module. No card.

Your first Future Fluency module is free. After that, your AI tutor is £70 a month.

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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

A Principal Scientist or Research Lead guides the technical direction of complex research programmes and shapes how the work gets done. This role means making the key scientific calls and setting the focus for a research effort.

4What you'd actually use

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

Python (NumPy, SciPy, Pandas, scikit-learn) Advanced

Designing complex statistical analyses, building custom data processing pipelines, developing predictive models for experimental outcomes, and automating routine data tasks for your team.

R (Tidyverse, ggplot2) Advanced

Performing advanced statistical modelling, creating publication-quality data visualisations, and developing reproducible research workflows for complex experimental data.

Benchling / Labguru (ELN/LIMS) Expert

Designing experiment templates, establishing data standards for your team, auditing records for IP and regulatory compliance, and ensuring comprehensive, searchable documentation across your programmes.

Scopus / Web of Science / PatSnap Advanced

Conducting complex Boolean searches for literature reviews and patent landscapes, setting up alerts for emerging IP, and synthesising findings into strategic insights for your programmes.

Jira / Confluence Expert

Configuring Jira workflows for your research programmes, building and maintaining the team's knowledge base architecture in Confluence, and championing adoption for efficient project tracking and knowledge sharing.

Tableau Server Basic

Providing clear data inputs for portfolio dashboards, interpreting existing visualisations, and understanding the metrics used to track R&D performance at a higher level.

Anaplan Basic

Providing accurate data inputs for your programme budgets, understanding the overall R&D budget model, and tracking your programme's financial performance against plan.

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
Experimental Design & Methodology Proposes designs based on established protocols, reviewed by Senior Scientist. Designs experiments independently, seeks peer review for complex cases. Leads design of novel experimental approaches, provides critical feedback to juniors.
Project 'Go/No-Go' Decisions Escalates findings to supervisor for decision. Recommends 'go/no-go' based on data, decision made by manager. Makes technical 'go/no-go' recommendations within workstream, consults manager.
Budget Allocation (Programme Level) No budget authority; requests resources from supervisor. Manages small project budget (<£5K) with manager approval. Recommends budget for workstream (£5K-£50K), requires Director approval.
Hiring & Team Development No involvement. Participates in interviews, provides feedback. Leads interviews, makes recommendations, mentors 0-2 junior staff.

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.

Programme Milestone Adherence
The percentage of critical research programme milestones delivered on or before the planned date.
Target · ≥90% of all major milestones completed on schedule.

If your 'Proof of Concept' milestone was set for 15 March, and you hit it on 10 March, that's a win. If three out of four programmes hit their milestones, that's 75% adherence for the quarter.

Intellectual Property Generation
The number of invention disclosures submitted and patent applications filed where you are a key contributor or inventor.
Target · Contribute to 2+ invention disclosures and 1+ patent filing per year.

In 2024, you led a programme that resulted in three novel discoveries, leading to two invention disclosures and one provisional patent application. That's hitting the target.

Technical De-risking Score
A quantifiable reduction in the technical risk profile of your assigned research programmes, often measured by TRL progression or successful completion of 'kill criteria' experiments.
Target · Achieve a 1-2 TRL level increase for 75% of active programmes annually, or successfully 'kill' 1-2 high-risk, low-potential projects.

Moving a programme from TRL 3 ('Experimental Proof of Concept') to TRL 5 ('Component Validation in Relevant Environment') within 12 months, demonstrating key technical hurdles have been overcome.

Team Mentorship & Development
The demonstrated growth and progression of your direct reports and mentees, evidenced by their increased autonomy, skill acquisition, and contributions.
Target · At least one direct report shows measurable skill improvement (e.g., takes on more complex tasks, leads a sub-project) within 12 months, or is ready for promotion.

One of your junior scientists, under your guidance, successfully designed and executed a complex DoE, something they couldn't do six months ago. That's a clear sign you're developing them.

Technical Leadership & Influence
Your ability to be recognised as a go-to technical expert, influencing strategic decisions and guiding the scientific direction of the department.
  • You're regularly consulted by other Principal Scientists or Managers on complex technical problems. Your recommendations are consistently adopted in programme design. You present at internal scientific forums, and people listen.
Cross-Functional Collaboration
How effectively you work with other teams (Product, Commercial, Legal) to ensure research aligns with business needs and IP strategy.
  • You're proactively involved in early-stage product discussions. Legal seeks your input on patent claims. Commercial teams come to you for scientific validation of market claims. You help bridge the 'Valley of Death' between research and development.
Problem-Solving & Adaptability
Your knack for tackling novel, ambiguous scientific challenges and adapting research plans when experiments don't go as expected (which, let's be honest, is often).
  • You can pivot a research programme effectively when initial hypotheses fail. You propose creative, data-driven solutions to unexpected technical roadblocks. You don't just identify problems
  • you solve them.
Quality of Scientific Communication
Your skill in clearly articulating complex scientific concepts and programme updates to both technical and non-technical audiences.
  • Your presentations are clear, concise, and compelling, whether to the R&D team or the executive committee. Your written reports are well-structured and easy to understand. You can explain the 'why' behind the 'what' to anyone.

7Would you like it

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

What people enjoy
Solving Complex Scientific Puzzles

You'll spend your days grappling with multi-variable problems, designing intricate experiments, and trying to understand the 'why' behind unexpected results. This isn't about routine; it's about pushing the boundaries of what we know.

You're faced with a novel material that shows unexpected properties. Your first thought isn't 'oh no,' it's 'how fascinating, how can we figure this out?' You'll dive into the literature, brainstorm with your team, and design a series of experiments to get to the bottom of it.

Building & Mentoring a High-Performing Team

You get a real kick out of seeing your team members grow, develop new skills, and make their own scientific contributions. You'll spend time guiding, teaching, and unsticking them, knowing their success is your success.

A junior scientist is struggling to interpret a complex dataset. Instead of just giving them the answer, you'll sit down, walk them through the process, and help them build the confidence to tackle it themselves next time. Their 'aha!' moment is genuinely rewarding for you.

Shaping the Future of Innovation

You're driven by the idea that your work today will lead to the products and technologies of tomorrow. You'll be thinking years ahead, identifying emerging trends and translating them into actionable research programmes.

You're constantly reading scientific journals, attending conferences, and talking to experts. You spot a nascent technology that could revolutionise our field in five years, and you immediately start sketching out a research programme to explore its potential, even if it's 'blue-sky' for now.

What frustrates people
  • The 'Urgent' Commercial Request: Constantly having to shield your team from 'quick questions' and 'small side projects' from the sales or marketing teams that derail meticulously planned, long-term experiments.
  • Short-termism Kills Innovation: Having to kill a promising long-term research project with massive potential to meet a quarterly budget cut dictated by the finance department.
  • The Burden of Justification: Spending 30% of your time creating reports and presentations to justify the existence and ROI of a department whose biggest wins may be a decade away.
  • Managing Unrealistic Expectations: The commercial team announces a product based on your TRL 4 technology as if it's ready to ship next quarter, creating immense pressure and unrealistic timelines for your team.
  • The IP Tightrope: The constant tension between encouraging scientists to publish their work to build their reputation and the critical need to protect discoveries for patent filings.
What this role does not give you
  • A predictable 9-to-5 routine – expect some late nights when experiments are running or deadlines loom.
  • Guaranteed success for every project – many will fail, and that's part of the process.
  • Complete autonomy over budget and resources – you'll need to make a strong case for every penny.
  • A direct, immediate link from your research to market launch – there's a long journey from lab to customer.

8Who you work with

You're directly responsible for the health and direction of key segments of our R&D pipeline. Your technical decisions and strategic guidance will either accelerate or derail the development of future products, directly affecting our competitive position and long-term revenue streams. Essentially, you're building the foundations for tomorrow's breakthroughs, and that's a pretty big deal.

Inside the business
  • Research Manager / Group Leader (your direct line)
  • Other Principal Scientists (your peers)
  • Product Development Leads (who'll take your validated tech)
  • Commercial & Marketing Teams (who need to understand the 'why')
  • Legal & IP Teams (for patent strategy)
  • Finance Department (for budget reviews)
Outside the business
  • Academic Collaborators (for cutting-edge research partnerships)
  • Key Vendors (for specialist equipment or services)
  • Strategic Partners (for joint development projects)
  • Industry Bodies (for standardisation or knowledge sharing)

9What you need before you start

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

  • Proven track record of leading complex scientific projects from conception to completion.
  • Demonstrated ability to mentor and technically guide junior scientists.
  • Extensive experience with advanced statistical analysis and experimental design (DoE).
  • Strong publication record or significant contribution to patent filings.
  • Comfortable presenting to senior management and cross-functional teams.
  • Experience managing a research budget, even if a smaller one.

10What to practise next

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

High-Throughput Experimentation (HTE) & Automation

The drive for faster discovery and more efficient resource use means that manual, one-off experiments are increasingly being replaced by automated, parallelised approaches. You'll need to design experiments that can leverage robotics and automated platforms.

Robotics & Liquid Handling Systems · Miniaturisation Techniques · Data Integration from Automated Systems · Statistical Power for HTE

  • This month: Read up on recent publications in automated experimentation relevant to your field.
  • Next quarter: Visit a facility with HTE capabilities or attend a webinar on lab automation.
  • Within 6 months: Propose a pilot HTE project for one of your research programmes, even if it's a small-scale automation.
  • Within 9 months: Work with a vendor or internal team to scope out the requirements for an HTE platform that could benefit your department.

Quick win: Identify one repetitive manual assay in your current workflow and brainstorm how it could be automated, even if you don't have the tools yet. This builds the mindset.

Digital Twins & Predictive Modelling for R&D

The ability to create virtual models of physical systems (digital twins) or processes allows for 'in silico' experimentation, dramatically reducing the need for costly and time-consuming physical experiments. This is about predicting outcomes before you even step into the lab.

Multi-Physics Simulation · Machine Learning for Predictive Models · Model Validation & Calibration · Uncertainty Quantification

  • This month: Explore open-source simulation tools or academic papers on digital twins in your domain.
  • Next quarter: Identify a specific experimental challenge in your programme that could benefit from predictive modelling.
  • Within 6 months: Collaborate with a data scientist or external consultant to build a simple predictive model for one aspect of your research.
  • Within 9 months: Present a case study on how predictive modelling reduced experimental cycles or costs in your programme.

Quick win: For your next experiment, try to predict the outcome using existing data and simple statistical models before running the experiment. Compare your prediction to the actual result.

11Staying current once you are in

What people here do to keep up
  • Regularly attending and presenting at international scientific conferences to stay current and build your professional network.
  • Publishing in peer-reviewed journals or contributing to industry white papers to maintain your scientific reputation.
  • Participating in internal leadership development programmes, especially those focused on influencing without direct authority or strategic thinking.
  • Mentoring junior scientists (formally or informally) to hone your leadership and coaching skills.
  • Engaging with industry consortia or special interest groups to contribute to broader scientific advancements and gain external perspectives.

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 & Advanced LLM Integration for Scientific Discovery

Competitors are already using advanced AI to draft research proposals, analyse literature, and even suggest novel molecular structures in minutes, tasks that used to take weeks. Scientists who master this will outproduce their peers significantly. This isn't just about ChatGPT; it's about integrating LLMs into our specific R&D workflows.

  • Context Windows & Token Limits
  • RAG (Retrieval Augmented Generation) Architectures
  • Output Validation & Hallucination Detection
  • Agentic AI for Experiment Design

Advanced Data Visualisation for Strategic Storytelling

With ever-increasing data complexity, the ability to distil insights into compelling, interactive visualisations is no longer just a 'nice to have' for analysts; it's critical for Principal Scientists to influence strategic decisions. Static charts won't cut it anymore.

  • Narrative Visualisation
  • Interactive Dashboards (e.g., Tableau, Power BI)
  • Perception & Cognition in Visualisation
  • Ethical Data Visualisation

13What you’ll use

What you’ll use

Skills this role draws on

Technical

  • Design of Experiments (DoE)
  • Technology Readiness Levels (TRL) Application
  • Stage-Gate® / Phase-Gate Process Management
  • Intellectual Property (IP) Strategy & Analysis
  • Scientific Literature Synthesis & Critical Evaluation
  • Research Programme Management

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

    From Senior Research Scientist

    3-5 years as a Senior Scientist

    Skills to master

    • Leading multiple complex projects, mentoring junior colleagues, taking ownership of scientific decisions, and effectively communicating with cross-functional teams.

    You're ready to move on when

    • Consistently delivering projects on time and budget with high scientific rigour.
    • Demonstrated ability to identify and solve novel scientific challenges independently.
    • Positive feedback from mentees and peers on your technical guidance and leadership.
    • Actively contributing to IP generation and scientific publications.
  2. 2

    From Academic Postdoctoral Researcher (with industry exposure)

    3-7 years of postdoc experience plus some industry collaboration/consulting

    Skills to master

    • Translating academic research into commercially viable solutions, understanding industrial R&D processes (like Stage-Gate), and adapting to faster-paced, goal-oriented environments.

    You're ready to move on when

    • A strong publication record in a relevant field.
    • Experience managing a small lab or research group (even if informal).
    • Evidence of collaborative projects with industry partners.
    • A clear understanding of the commercialisation pathway for scientific discoveries.
  3. 3

    From a Deep Technical Specialist (e.g., Data Scientist, Computational Chemist)

    8-12 years in a specialist role with increasing project leadership

    Skills to master

    • Broadening scientific domain knowledge beyond your specialism, developing strong people leadership skills, and understanding the end-to-end R&D process.

    You're ready to move on when

    • You're the go-to person for complex technical challenges in your specialism.
    • You've started leading multi-disciplinary projects that require collaboration beyond your immediate team.
    • You've taken on informal mentorship roles and enjoy helping others grow.
    • You're actively seeking to understand the 'bigger picture' of our R&D strategy.

15Where this role leads

The long view: This work can lead to broader research leadership, such as Director of Research or VP of R&D, or into roles that turn research into new ventures.

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 Principal Scientist / Research Lead is actually changing.

Where it leads

Roles this leads to

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

  • Staff Scientist / Distinguished Scientist (L5 - Individual Contributor)

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 Introduction to Data Science and Big Data, worked against Principal Scientist / Research Lead.

Introduction to Data Science and Big Data Level 5

Applied to your work in Principal Scientist / Research Lead

The objective of this unit is to provide learners with a systematic understanding of Data Science and Big Data concepts, including their characteristics and applications. Learners will develop proficiency in data collection, design, and modelling techniques, and will be able to select appropriate tools for data pre-processing and apply analytical techniques to generate insights from data.

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
Principal Scientist / Research Lead
  • Design of Experiments (DoE)
  • Technology Readiness Levels (TRL) Application
  • Stage-Gate® / Phase-Gate Process Management
  • Intellectual Property (IP) Strategy & Analysis
  • Scientific Literature Synthesis & Critical Evaluation
  • Research Programme Management
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

Principal Scientist / Research Lead is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

  1. Research Manager / Group Leader (L5)

    3-5 years in the Principal Scientist role

    This is a step into formal people management, overseeing multiple Principal Scientists and their programmes.

    • Advanced Stakeholder Management (managing conflicting priorities across departments)
    • Vendor & Partner Management (negotiating and overseeing external collaborations)
    • Performance Management (for a team of managers/senior individual contributors)
    • Risk Management (at a portfolio level)

  2. Staff Scientist / Distinguished Scientist (L5 - Individual Contributor)

    3-7 years in the Principal Scientist role

    This is an individual contributor path, focusing on deep technical expertise and innovation without formal people management.

    • Complex Problem Architecting (designing solutions for the most intractable scientific challenges)
    • Advanced Mentorship (mentoring other Principal Scientists or Leads)
    • IP Portfolio Strategy (contributing to the overall company IP strategy)
    • Scientific Due Diligence (for M&A or strategic partnerships)
Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, a lot of research involves sifting through mountains of information, drafting reports, and trying to spot patterns in complex data. What if you could offload a significant chunk of that grunt work to AI? This isn't about replacing you; it's about making you a far more powerful and efficient Principal Scientist.

As a Principal Scientist, your brainpower is best spent on high-level scientific strategy, experimental design, and critical interpretation. AI tools can handle the repetitive, time-consuming tasks, freeing you up to focus on the truly innovative work—the stuff that only a human expert can do. Think of AI as your ultimate research assistant, always available and tirelessly efficient.

Automated Literature Synthesis

Use AI tools like Scite or Elicit to automatically screen, summarise, and categorise thousands of research papers. You can identify key themes, methodologies, and knowledge gaps in minutes, instead of weeks spent manually reviewing. This means faster, more comprehensive background research for your programmes.

Hypothesis Co-Pilot & Data Exploration

Leverage generative AI and machine learning models to analyse massive internal or public datasets (genomic, chemical, materials data). These tools can propose novel, non-obvious hypotheses for your team to investigate, accelerating your discovery phase by spotting connections you might miss.

Accelerated IP Analysis

Employ AI-powered patent search tools (like PatSnap or Derwent Innovation) to conduct prior art searches and initial Freedom-to-Operate (FTO) analysis in a fraction of the time. This allows for faster and more informed decisions on patentability and helps you strategically direct your R&D efforts to avoid existing IP.

Grant & Report Drafter

Use Large Language Models (LLMs) as a first-pass drafter for grant proposals, internal progress reports, and patent disclosures. The AI can structure the document, write boilerplate sections, and even suggest wording, freeing up you and your scientists to focus on refining the core scientific narrative and critical details.

Common questions

Common questions

How do you become a Principal Scientist / Research Lead?

Common routes in include From Senior Research Scientist (3-5 years as a Senior Scientist), From Academic Postdoctoral Researcher (with industry exposure) (3-7 years of postdoc experience plus some industry collaboration/consulting) and From a Deep Technical Specialist (e.g., Data Scientist, Computational Chemist) (8-12 years in a specialist role with increasing project leadership). Times vary with prior experience.

Where can a Principal Scientist / Research Lead progress to?

This role can lead on to Research Manager / Group Leader (3-5 years in the Principal Scientist role) and Staff Scientist / Distinguished Scientist (3-7 years in the Principal Scientist role), depending on the skills you build.

What new skills matter most for a Principal Scientist / Research Lead?

Increasingly, Prompt Engineering & Advanced LLM Integration for Scientific Discovery and Advanced Data Visualisation for Strategic Storytelling. These are the skills our record of this role lists as growing.

Free tools

Want a read on where you stand?

Three free tools, no account needed. Each one ends with a role, a level and a way in.

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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 research and development

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

The skills built in this role, deep scientific expertise, programme leadership, IP strategy and cross-functional influence, carry across industries. They can move into R&D leadership in pharma, biotech, advanced materials or consumer goods, or into scientific consulting, venture capital focused on deep tech, or a government research agency.

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 Automation 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.