UK

United Kingdom · Technology and data · Entry Level (0-2 years)

Associate Natural Language Processing Specialist

As an Associate Natural Language Processing Specialist, you transform raw data into the building blocks of smarter systems.

Here is the whole job, in plain words. What it is, a real day, 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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Free to start: a plan built for this role, and a Future Fluency module to try first. No card.

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One email: what AI is changing in this job, the new skills it asks for, where it leads, and where to start free. With a link back here. Nothing else follows unless you confirm your address.

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  • Experience bandEntry Level (0-2 years)
  • Direct reportsNo direct reports
  • Reports toSenior Natural Language Processing Specialist
  • UK framework levelUsually someone starting out, or keeping a process running

Also advertised as Junior NLP Engineer · NLP Assistant · Data Science Trainee (NLP focus)

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

Qualifi Level 3 Extended Diploma in IT and Data Science Qualifi Ltd · Level 3 · covers 1 of 4 standards
Qualifi Level 3 Diploma in Data Science Qualifi Ltd · Level 3 · covers 1 of 4 standards
LRN Level 3 Award in Artificial Intelligence Competence Learning Resource Network · Level 3 · 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
NOCN Level 2 Award 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

Machine Learning Methods and Models in Data Science Qualifi Level 3 Extended Diploma in IT and Data Science
Artificial Intelligence Project Design & Communication LRN Level 3 Award in Artificial Intelligence Competence
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
Applying AI in the Workplace NOCN Level 2 Award in AI Awareness

The future skills this job leans on

AI Fluency working with AI as a thinking partner
Automation Fluency building AI into how work runs
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Your Plan Illustration

Built for Associate Natural Language Processing Specialist

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

  1. Machine Learning Methods and Models in Data Science Qualifi Ltd · covers 1 of 4 standards Level 3
  2. Artificial Intelligence Project Design & Communication Learning Resource Network · covers 1 of 4 standards Level 3
  3. Using Artificial Intelligence in Business SIAS · covers 1 of 4 standards Level 2
  4. AI and Your Career NOCN · covers 1 of 4 standards Level 2
  5. Applying AI in the Workplace 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 an Associate Natural Language Processing Specialist?

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

How the AI economy is changing work like this

A broad read on this kind of work, not an analysis of this job on its own. Roles that share a pattern get the same answer here.

Fading: AI does more of this

AI is taking over the repetitive task of running scripts and initial data cleaning, freeing you to focus on more nuanced tasks like data annotation and error analysis.

→
Rising: worth more because of AI

Your ability to interpret and refine AI outputs becomes increasingly valuable, as judgement and insight guide improvements in model accuracy and application.

The new skills this role is being asked for

  • Prompt Engineering Fundamentals
  • Basic MLOps for NLP
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 Associate Natural Language Processing Specialist plan as you set up, starting with a Future Fluency module on using AI in your job. No card.

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

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3What this role really is

We see you

You often wonder if AI will outpace your own learning, but you're driven by the challenge of teaching machines to understand the nuances of human language. There's a quiet satisfaction in knowing your meticulous work lays the foundation for complex algorithms.

This job involves helping build and refine natural language processing models that let computers work with human language.

4A day in the life

Not a job advert. A real day, built from what this role actually holds.

08:45
You start your day by executing Python scripts to clean and normalise text data, ensuring it's ready for model training.
11:00
You dive into data annotation, labelling entities with precision using tools like Labelbox, while sipping your mid-morning tea.
14:30
In a team meeting, you share observations about the latest model behaviour, raising a few questions that spark a lively discussion.
16:00
You document your day's findings, making sure future-you can easily pick up the thread when debugging later.

5What you'd actually use

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

Python (pandas, NumPy, scikit-learn) Intermediate

Writing scripts for data cleaning, basic analysis, and executing model training pipelines. You'll be using pandas for data manipulation constantly.

Hugging Face Transformers Intermediate

Loading pre-trained models, running basic inference, and fine-tuning models on new datasets following existing scripts. You won't be building architectures from scratch yet.

spaCy / NLTK Intermediate

Performing tokenisation, part-of-speech tagging, named entity recognition (NER), and other text preprocessing tasks. These are your workhorses for text manipulation.

Labelbox / Prodigy Intermediate

Annotating text data for various NLP tasks (e.g., sentiment, entity extraction) and reviewing annotations from others to ensure quality.

AWS SageMaker (notebooks) Intermediate

Running Python scripts for data preparation and model training within a cloud environment, managing your own notebook instances.

Basic SQL Basic

Pulling data from our internal databases for analysis or model training. You'll need to write simple SELECT statements.

6What 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
Data Cleaning Methodology Follows pre-defined scripts and guidelines; escalates any new data anomalies or edge cases to Senior NLP Specialist for guidance. Chooses appropriate cleaning methods for new datasets based on common patterns; consults Senior NLP Specialist for complex or novel data issues. Designs and implements new data cleaning pipelines; defines best practices and guidelines for the team; makes final decisions on complex data normalisation strategies.
Tool/Library Selection for Task Uses specified tools/libraries (e.g., NLTK for tokenisation, spaCy for NER) as directed by Senior NLP Specialist. Proposes alternative libraries for routine tasks if they offer efficiency gains; seeks approval from Senior NLP Specialist. Selects and evaluates new NLP libraries and tools for project use; makes recommendations for team-wide adoption based on technical merits and project needs.
Model Training Parameters Executes training with parameters provided by Senior NLP Specialist; reports any deviations or unexpected outcomes. Adjusts hyperparameters within a defined range to optimise model performance for specific tasks; consults Senior NLP Specialist on significant changes. Determines optimal training parameters and strategies; designs hyperparameter tuning experiments; makes final decisions on model configuration before deployment.
Project Timelines & Scope Changes Reports any potential delays or scope creep on assigned tasks to Senior NLP Specialist immediately. Communicates potential timeline impacts for their owned tasks; proposes adjustments to their work plan; seeks approval for any significant changes. Negotiates timelines with product managers; makes recommendations for scope adjustments to meet deadlines; informs Director of any major project risks.

7How 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.

Data Annotation Accuracy
How accurately you label text data according to our guidelines.
Target · >95% agreement with senior reviews

You annotated 100 documents; a senior specialist reviewed 5 and found 1 minor discrepancy, putting you at 98% accuracy for that batch.

Data Preprocessing Throughput
The volume of raw text data you successfully clean and prepare for model training.
Target · Process 500-1,000 documents weekly

You took a raw dataset of 750 customer reviews, applied all necessary cleaning scripts, and formatted it correctly for the next stage, all within the week.

Model Training Script Execution Success Rate
How often your execution of pre-written model training scripts runs without errors or requiring intervention.
Target · >98% success rate

Out of 20 training runs you initiated last month, 19 completed without you needing to fix a configuration error or debug a script issue.

Documentation Contribution
How much you contribute to keeping our internal guides and wikis up-to-date, especially for tasks you've learned.
Target · 1-2 new or updated entries monthly

You added a new section to the 'Data Cleaning Best Practices' wiki page, detailing a new regex pattern you found useful, and updated the 'Running Basic Classification Models' guide after a software update.

Proactive Learning & Curiosity
Your willingness to ask questions, seek out new information, and understand the 'why' behind tasks.
  • Regularly asks clarifying questions during stand-ups
  • independently researches new NLP concepts or tools
  • shares interesting articles or findings with the team
  • actively participates in internal learning sessions.
Attention to Detail in Data Handling
Your ability to spot inconsistencies, errors, or edge cases in text data before they become bigger problems.
  • Identifies subtle annotation errors during review
  • points out unexpected data formats in raw inputs
  • meticulously checks script outputs for anomalies
  • catches minor typos in documentation.
Responsiveness to Feedback
How well you take on board constructive criticism and apply it to your subsequent work.
  • Incorporates feedback from code reviews into next pull request
  • shows improvement on recurring issues after coaching
  • actively seeks feedback on tasks
  • isn't defensive when mistakes are pointed out.
Team Collaboration & Support
How effectively you work with your immediate team, asking for help when needed and offering it when you can.
  • Communicates blockers early
  • offers to help colleagues with tasks they're stuck on (within your capability)
  • shares knowledge gained
  • participates constructively in team discussions.

8Would you like it

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

What people enjoy
Solving Puzzles

You get a real kick out of debugging a tricky script, figuring out why a model isn't performing, or finding the perfect regex pattern to clean some messy text. It's the challenge of finding the solution that drives you.

Spending an hour meticulously crafting a complex regex to extract specific entities from unstructured text, and feeling a sense of accomplishment when it finally works perfectly across all edge cases.

Continuous Learning

You're energised by picking up new Python libraries, understanding a new NLP concept, or seeing how an experienced specialist approaches a problem. You actively seek out new knowledge.

After a team meeting where a new vector database was mentioned, you immediately go home and start reading its documentation or watching a tutorial, just to get a head start.

Building Foundational Tech

You enjoy the process of preparing data, setting up experiments, and running training jobs, knowing that your meticulous work is the bedrock for powerful AI features. You appreciate the importance of the groundwork.

You take pride in ensuring a dataset is perfectly clean and labelled, knowing that this attention to detail is what will allow a senior colleague to build a highly accurate model on top of it.

What frustrates people
  • Spending 80% of your time cleaning data, not building models.
  • Running the same script multiple times because of minor configuration tweaks.
  • Having to ask for clarification on annotation guidelines repeatedly.
  • Seeing a senior colleague easily debug something you've been stuck on for hours.
  • The sheer volume of new papers and tools in NLP – it can feel overwhelming to keep up.
What this role does not give you
  • Full autonomy on projects
  • Strategic decision-making authority
  • Direct management of other team members
  • Immediate responsibility for production systems
  • A purely research-focused environment (this is applied NLP)

9Who you work with

This role directly supports the development of our core NLP capabilities. Your work ensures that the data used for training is clean and correctly labelled, which is absolutely fundamental to building any useful language model. Get it right, and our models are robust; get it wrong, and we're building on shaky ground, leading to unreliable features and wasted effort down the line.

Inside the business
  • Senior NLP Specialists
  • Data Engineers
  • Product Managers (for data understanding)
  • QA Testers (for model output review)
Outside the business
  • None directly (this is an internal-facing role at this level)

10What you need before you start

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

  • A solid grasp of Python programming fundamentals (data structures, control flow, functions, basic OOP).
  • Experience with at least one major machine learning library (e.g., scikit-learn, TensorFlow, PyTorch) from academic projects or internships.
  • Familiarity with command-line interfaces and basic Linux commands.
  • A genuine curiosity about how language works and how computers can understand it.
  • The ability to learn quickly and adapt to new tools and methodologies.

11What to practise next

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

Transformer Architectures (Deeper Dive)

Transformers are the backbone of modern NLP. While you'll use them at Associate level, you'll need to understand their inner workings better to fine-tune them effectively and troubleshoot issues.

Attention mechanism · Encoder-decoder models · Decoder-only models · Positional encodings

  • This week: Watch a few YouTube videos explaining the 'Attention Is All You Need' paper (the original Transformer paper).
  • This month: Try to implement a very basic attention mechanism from scratch in PyTorch or TensorFlow (even if it's just a toy example).
  • Month 2: Read the Hugging Face documentation on different Transformer architectures and their use cases.
  • Month 3: Experiment with fine-tuning different Transformer models (e.g., BERT, RoBERTa) for a simple classification task and compare their performance.

Quick win: Whenever you use a Hugging Face model, take 10 minutes to read its specific documentation and try to understand its architectural overview.

Vector Databases & Embeddings

Retrieval-Augmented Generation (RAG) is becoming standard for grounding LLMs with proprietary data. Understanding how to store and retrieve text embeddings efficiently is crucial for building these systems.

Text embeddings · Vector search · Indexing strategies (basic) · Pinecone / Weaviate (basic usage)

  • This week: Read an introductory article on text embeddings and vector databases.
  • This month: Try a free tier of Pinecone or Weaviate and load some simple text embeddings into it, then perform a basic search.
  • Month 2: Experiment with different embedding models (e.g., Sentence-BERT) and compare their performance on a similarity task.
  • Month 3: Build a very simple RAG prototype using a small local LLM and a vector database for a personal project.

Quick win: Whenever you're working with text, think about how you could represent it as an embedding and what kind of information that embedding would capture.

12Staying current once you are in

What people here do to keep up
  • Participating in Kaggle competitions (especially text-based ones) to get hands-on experience with real-world data.
  • Contributing to open-source NLP projects on GitHub.
  • Attending local NLP meetups or online webinars to network and learn from others.
  • Maintaining a personal blog or portfolio to showcase your projects and learning journey.
  • Reading key NLP research papers (start with survey papers!) to stay informed about the latest advancements.

13The 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 Fundamentals

Large Language Models (LLMs) are everywhere now, and knowing how to talk to them effectively is becoming a core skill for any NLP role. It's about getting the most out of these powerful tools, even if you're not building them.

  • Zero-shot / Few-shot prompting
  • Instruction tuning
  • Chain-of-Thought prompting
  • Role prompting

Basic MLOps for NLP

It's not enough to build a model; you need to know how to get it into production and keep it running reliably. Understanding the deployment lifecycle, even at a basic level, will make you a much more valuable team member.

  • Model packaging (Docker basics)
  • Experiment tracking (MLflow/W&B)
  • Model versioning
  • Basic monitoring concepts

14What you’ll use

What you’ll use

Skills this role draws on

Technical

  • Classical NLP Techniques
  • Text Preprocessing
  • Basic Machine Learning Concepts
  • Data Annotation Principles
  • Version Control (Git)

15The 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

    University Graduate (CS/AI/Linguistics)

    0-1 year post-graduation

    Skills to master

    • Practical Python coding, Git version control, basic data cleaning, understanding of ML fundamentals, ability to follow complex instructions.

    You're ready to move on when

    • Strong academic project portfolio (e.g., final year project involving NLP).
    • Internship experience in a data or software role.
    • Demonstrable personal projects on GitHub.
    • A clear understanding of basic NLP concepts.
  2. 2

    Coding Bootcamp Graduate (NLP Specialism)

    0-1 year post-bootcamp

    Skills to master

    • Hands-on experience with Python NLP libraries, understanding of model training workflows, ability to work in a team, strong problem-solving skills.

    You're ready to move on when

    • Successfully completed a rigorous NLP-focused bootcamp.
    • Developed a capstone project demonstrating practical NLP application.
    • Comfortable with daily coding challenges and rapid learning.
    • Can articulate how they approached and solved real-world data problems.
  3. 3

    Data Analyst / Junior Data Scientist (transition)

    1-2 years in previous role

    Skills to master

    • Strong data manipulation skills (pandas, SQL), basic ML knowledge, desire to specialise in text data, ability to pick up new domain-specific tools quickly.

    You're ready to move on when

    • Has worked with unstructured text data in previous roles (even if not NLP-specific).
    • Shows a clear passion for language and AI.
    • Has taken online courses or built personal projects in NLP.
    • Can demonstrate a solid understanding of data pipelines and quality.

16How people get here · where they go next

Came from
University Graduate (CS/AI/Linguistics)
0-1 year post-graduation
You mastered the basics of NLP and Python coding, and learned to follow complex instructions in academic projects.
You are here
Associate Natural Language Processing Specialist
Entry Level (0-2 years)
This job involves helping build and refine natural language processing models that let computers work with human language.
Goes to
NLP Specialist (Level 2)
2-3 years
This job involves independently owning NLP features or models, taking full responsibility for their lifecycle and improvements.

The long view: This work builds a foundation in natural language processing. It can lead to work as a senior NLP specialist, taking on more complex projects, or as a principal NLP scientist, shaping technical direction.

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 Associate Natural Language Processing Specialist 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:

  • NLP Specialist (Level 2)
  • Data Engineer (with NLP focus)

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 Machine Learning Methods and Models in Data Science, worked against Associate Natural Language Processing Specialist.

Machine Learning Methods and Models in Data Science Level 3

Applied to your work in Associate Natural Language Processing Specialist

The objective of this unit is to provide learners with a foundational understanding of machine learning methods and models used in data science. Learners will gain knowledge of supervised, unsupervised, and reinforcement learning, including their applications and key characteristics.

The Explorer Last time, we explored using spaCy for entity recognition. How did the experiment with your dataset go?

You It was challenging, but I managed to label a few entities correctly.

The Explorer Great, let's build on that by trying to fine-tune the model parameters to improve accuracy on your specific 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.

Start my plan, free
A year from now

A year from now, you're confidently navigating the intricacies of NLP models, contributing insights that shape smarter systems.

See Your Progress Grow Illustration
Associate Natural Language Processing Specialist
  • Classical NLP Techniques
  • Text Preprocessing
  • Basic Machine Learning Concepts
  • Data Annotation Principles
  • Version Control (Git)
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.

17The 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

Associate Natural Language Processing Specialist is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

  1. NLP Specialist (Level 2)

    2-3 years (from Associate)

    You'll move from executing tasks under guidance to independently owning well-defined features or models. You'll take full responsibility for the lifecycle of smaller NLP components.

    • Designing simple annotation schemas.
    • Debugging and optimising existing model training pipelines.
    • Proposing and implementing minor model improvements.
    • Deeper understanding of Transformer architectures and fine-tuning techniques.

  2. Data Engineer (with NLP focus)

    3-4 years (from Associate)

    This path shifts you more towards building and maintaining the infrastructure that supports NLP models, rather than the models themselves. You'd be responsible for data pipelines, MLOps, and scalable systems.

    • Building robust data ingestion and cleaning pipelines (e.g., using Apache Spark).
    • Developing and maintaining MLOps infrastructure (e.g., Docker, Kubernetes, MLflow).
    • Optimising data storage and retrieval for text data (e.g., vector databases).
    • Implementing API endpoints for model serving.
Working with AI on the job

Working with AI

Where AI is starting to help

Honestly, a lot of the initial work in NLP can be a bit repetitive. But here's the good news: you won't be doing it all manually. We're big believers in using AI to make our own lives easier, and that means you'll have access to tools that can automate some of the more mundane tasks, letting you focus on the interesting stuff and learn faster.

As an Associate NLP Specialist, you'll be learning the fundamentals, and that involves a fair bit of boilerplate code, data wrangling, and reading up on new research. We've got AI tools ready to help you shave hours off these tasks, meaning you get to spend more time understanding why things work, rather than just how to type them out.

Boilerplate Code Generation

Use GitHub Copilot or similar AI assistants to instantly generate common code snippets. Think data loading, basic PyTorch/TensorFlow training loops, or even simple REST API endpoints. It's like having a super-fast coding buddy.

Research Paper Summarisation

Feed those dense arXiv papers into an LLM tool to get quick summaries, extract key methodologies, and identify how new techniques might apply to your current projects. Cut down on reading time and get to the core ideas faster.

Architectural Brainstorming Assistant

Use a conversational AI as a sounding board. Ask it questions like, 'What are the pros and cons of different tokenisation strategies for legal text?' or 'How would I structure a basic sentiment analysis pipeline?' It's a great way to learn and explore ideas.

Automated Documentation & Comments

Point a code-aware LLM at your completed scripts and have it automatically generate detailed READMEs, function docstrings, and in-line comments. Spend less time writing about your code and more time writing actual code.

Common questions

Common questions

How do you become an Associate Natural Language Processing Specialist?

Common routes in include University Graduate (CS/AI/Linguistics) (0-1 year post-graduation), Coding Bootcamp Graduate (NLP Specialism) (0-1 year post-bootcamp) and Data Analyst / Junior Data Scientist (transition) (1-2 years in previous role). Times vary with prior experience.

Where can an Associate Natural Language Processing Specialist progress to?

This role can lead on to NLP Specialist (2-3 years (from Associate)) and Data Engineer (with NLP focus) (3-4 years (from Associate)), depending on the skills you build.

What new skills matter most for an Associate Natural Language Processing Specialist?

Increasingly, Prompt Engineering Fundamentals and Basic MLOps for NLP. 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.

Read the Zavmo manifesto to understand how you will be taught Meet your tutors

Or try another one

18Where to go from here

Other roles at Level 2

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 in NLP, machine learning and data engineering carry across many industries, including tech, finance, healthcare, media and government. Demand for people who can work with language data and build intelligent systems continues to grow.

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.