UK

United Kingdom · Technology and data · Senior (5-8 years)

Senior AI Data Scientist Assistant

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

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  • Experience bandSenior (5-8 years)
  • Direct reportsNo direct reports
  • Reports toLead Data Scientist or Data Science Manager
  • UK framework levelUsually a coordinator, or early in a professional job

Also advertised as Senior Data Wrangler · Data Preparation Specialist (AI Focus) · AI Data Operations Lead · Advanced Data Support Engineer

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

NOCN Level 4 certificate for Data Analysts NOCN · Level 4 · covers 7 of 10 standards
Pearson BTEC Level 4 Higher National Certificate in Computing for England Pearson Education Ltd · Level 4 · covers 3 of 10 standards
LRN Level 3 Award in Artificial Intelligence Competence Learning Resource Network · Level 3 · covers 1 of 10 standards
Qualifi Level 4 Diploma in AI Qualifi Ltd · Level 4 · covers 1 of 10 standards
Pearson BTEC Level 5 Higher National Diploma in Computing for England Pearson Education Ltd · Level 5 · covers 5 of 10 standards
NCC Education Level 5 Extended Diploma in Computing with Data Science NCC Education Limited · Level 5 · covers 4 of 10 standards

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

The units you would start with

Practical Data Science NOCN Level 4 certificate for Data Analysts
Data Analytics Pearson BTEC Level 5 Higher National Diploma in Computing for England
Artificial Intelligence Project Design & Communication LRN Level 3 Award in Artificial Intelligence Competence
Introduction to Artificial Intelligence and Applications Qualifi Level 4 Diploma in AI
Artificial Intelligence NCC Education Level 5 Extended Diploma in Computing with Data Science

The future skills this job leans on

AI Fluency working with AI as a thinking partner
Data Fluency turning numbers into a decision
Automation Fluency building AI into how work runs
Systems Fluency seeing the whole picture
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Your Plan Illustration

Built for Senior AI Data Scientist Assistant

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

  1. Practical Data Science NOCN · covers 7 of 10 standards Level 4
  2. Data Analytics Pearson Education Ltd · covers 5 of 10 standards Level 5
  3. Artificial Intelligence Project Design & Communication Learning Resource Network · covers 1 of 10 standards Level 3
  4. Introduction to Artificial Intelligence and Applications Qualifi Ltd · covers 1 of 10 standards Level 4
  5. Artificial Intelligence NCC Education Limited · covers 4 of 10 standards Level 5
These are the real units behind this job, in the order they rank for it. Nothing here is marked done, because this plan has not been started by anyone yet. Yours would fill in as you go.

2AI and this work

Will AI change your work as a Senior AI Data Scientist Assistant?

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

  • Advanced Prompt Engineering & LLM Integration for Data Tasks
  • Cloud-Native Data Orchestration (Advanced)
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 Senior AI Data Scientist Assistant 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

This job involves preparing and cleaning data so it can be used in AI models. You untangle messy datasets, build data pipelines, and keep them running so data scientists can rely on the results.

4What 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, Matplotlib, Seaborn) Advanced

You'll be writing complex Python scripts for data cleaning, transformation, feature engineering, and automated reporting. You'll use `pandas` for advanced data wrangling, `NumPy` for numerical operations, and `scikit-learn` for pre-processing and model evaluation support. Visualisations will be built with `Matplotlib` and `Seaborn`.

SQL (PostgreSQL, MySQL, Snowflake) Advanced

You'll be authoring complex queries with CTEs (Common Table Expressions), window functions, and subqueries across various database systems. Debugging and optimising slow-running queries will be second nature to you. You'll also use SQL for data validation and schema exploration.

Git (GitHub/GitLab) Advanced

You'll be comfortable with command-line Git, managing branches, handling merge conflicts, and conducting thorough code reviews via pull requests on GitHub or GitLab. You'll ensure our codebase is clean, version-controlled, and collaborative.

Jupyter Notebooks & VS Code Advanced

You'll be creating well-documented Jupyter Notebooks from scratch for exploratory data analysis (EDA), prototyping data transformations, and sharing insights. You'll also refactor notebook code into reusable Python scripts using VS Code, ensuring maintainability and reproducibility.

Data Platforms (AWS S3, Databricks, Snowflake) Advanced

You'll programmatically interact with AWS S3 for data storage and retrieval. You'll use platforms like Databricks or Snowflake to query, process, and transform larger datasets, often writing complex SQL or Spark code. You'll understand how to work efficiently with distributed data.

Visualization Tools (Tableau, Power BI) Advanced

You'll be building complex, interactive dashboards in Tableau or Power BI. This includes using calculated fields, parameters, and advanced chart types to provide deep-dive capabilities and tell compelling data stories. You'll ensure our visualisations are clear, accurate, and actionable.

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
Data Cleaning Methodology Follow prescribed methods; escalate any deviation or complex cases. Choose appropriate methods for routine problems; consult on novel issues. Design and implement optimal cleaning strategies for complex, ambiguous datasets; define best practices for the team.
SQL Query Optimisation Run existing queries; flag slow performance to supervisor. Optimise routine queries for performance; seek guidance for complex cases. Architect and optimise complex queries with CTEs/window functions; debug and resolve performance bottlenecks independently; mentor others on query optimisation.
Tool/Library Selection for Data Prep Use tools as instructed (e.g., pandas, NumPy). Select appropriate standard libraries for a given task. Evaluate and recommend new Python libraries or tools for specific data preparation challenges; justify choices based on performance and maintainability.
Mentoring & Guidance Ask questions, absorb knowledge. Provide informal help to new joiners on basic tasks. Act as a formal mentor for 1-2 junior assistants, providing structured guidance, code reviews, and unblocking support.

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.

Data Pipeline Reliability
Uptime and successful completion rate of data pipelines you own or significantly contribute to.
Target · 99.5% successful runs (excluding planned maintenance)

If your daily ETL script runs 20 times a month, we'd expect no more than one failure. Anything above that means a deeper look into the root cause is needed.

Data Preparation Time Savings for Lead Scientists
Quantifiable reduction in time Lead Data Scientists spend on data cleaning and feature engineering for projects you support.
Target · Save Lead Scientists 5+ hours per week per project

A Lead Data Scientist estimates they spent 8 hours less on data prep for a new model because of your automated scripts and clean datasets. That's a win.

Data Quality Issue Resolution Rate
The percentage of identified data quality issues (e.g., missing values, incorrect types, outliers) that you resolve or escalate with a clear plan.
Target · 95% resolution or documented escalation within agreed SLAs

You spot 10 data quality issues in a new dataset. You fix 9 of them and clearly document the 1 remaining, explaining why it's an upstream problem for the data engineering team to tackle.

Mentee Productivity Ramp-Up
The speed at which junior assistants you mentor become independently productive on common tasks.
Target · Junior assistants achieve 80% independent productivity on core tasks within 3 months

Your mentee, after 3 months, can independently pull sales data, clean it, and generate a basic report without needing you to hold their hand. That's good mentoring.

Proactive Problem Identification
You're not just fixing problems, you're spotting them before they become big issues. This means anticipating data quality problems or potential model biases.
  • You're regularly bringing potential data issues to the team's attention before they're noticed downstream. You'll suggest improvements to data collection or storage. Others will say things like, 'Thank goodness [Your Name] caught that early!'
Documentation Quality & Reusability
Your code and data pipelines are well-documented, easy to understand, and designed so others can pick them up and run with them.
  • Team members can easily use your scripts or understand your notebooks without needing to ask you dozens of questions. Your documentation is clear, concise, and kept up-to-date. You'll get comments like, 'This notebook is a lifesaver, so clear!'
Stakeholder Trust & Collaboration
You're seen as a reliable and helpful partner by Lead Data Scientists and other teams, someone they trust with their trickiest data challenges.
  • Lead Data Scientists will specifically ask for your help on new projects. Other teams will come to you directly with complex data requests, knowing you'll get it right. You're seen as a bridge between data sources and analytical needs, not just a pair of hands.
Mentorship Effectiveness
Your ability to guide and develop junior team members, helping them grow their skills and confidence.
  • Junior assistants you work with show clear improvement in their technical skills and problem-solving abilities. They feel comfortable asking you questions and learning from your feedback. They'll tell their manager how much they've learned from you.

7Would you like it

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

What people enjoy
Solving Complex Puzzles

You get a real buzz from untangling a gnarly dataset, figuring out why a pipeline broke, or optimising a slow SQL query. The more complex the data challenge, the more engaged you are.

Spending an afternoon debugging a multi-stage data transformation that's been failing for days, and finally getting it to run perfectly, feels like a major accomplishment.

Enabling Others' Success

You love being the person who makes it possible for the Lead Data Scientists to build amazing models. Knowing your clean data is the foundation of their breakthrough work is deeply satisfying.

When a Lead Data Scientist tells you their new model is performing brilliantly, and your data prep was 'spot on,' you feel a genuine sense of pride and contribution.

Building Robust Systems

You enjoy designing and implementing automated data pipelines and processes that are reliable, efficient, and scalable. You want to build things that just 'work' consistently.

You've just finished automating a weekly data report that used to take hours of manual effort, and now it runs flawlessly every Monday morning without you touching it.

What frustrates people
  • The 80/20 rule is real: Expect to spend up to 80% of your time finding, cleaning, and preparing data, and only 20% on the more 'interesting' analysis or model support tasks. It's often grunt work.
  • Vague scoping: You'll frequently receive ambiguous requests like, 'Can you pull the sales data for last quarter?' forcing you to go back and ask multiple clarifying questions about regions, product lines, customer segments, and specific definitions.
  • The moving goalposts: After you've spent a day running a complex query and cleaning the data, the stakeholder will realise they forgot a key filter or want to add a different data source, forcing you to start over.
  • Upstream data nightmares: An engineering team will change a schema or an API endpoint without warning, causing your data pipelines to fail spectacularly at 8 AM. You are often the first line of defence.
  • The unsung hero: You will perform the foundational data prep that makes a high-impact model possible, but the data scientist will almost always get the public credit and recognition. You're the engine, not the driver.
  • Data scavenger hunts: You'll be asked to analyse the impact of a marketing campaign using data that only exists in a CSV file on a marketing manager's laptop, forcing you to bypass official data sources and get creative.
What this role does not give you
  • Direct ownership of complex machine learning model development or deployment (that's the Lead Data Scientist's job).
  • Consistent, predictable work with no urgent, last-minute requests.
  • A role where you're always in the spotlight; much of your work is foundational and behind-the-scenes.
  • A quiet, uninterrupted environment; you'll be collaborating and responding to queries constantly.

8Who you work with

This role directly underpins the success of our AI and machine learning initiatives. By ensuring data quality and availability, you enable faster model development, more reliable predictions, and ultimately, better data-driven decisions across the organisation. Without you, our data scientists would spend all their time on data plumbing, rather than innovation. You're essentially the unsung hero making sure the data engine runs smoothly for everyone else.

Inside the business
  • Lead Data Scientists (your primary 'clients')
  • Data Engineers (for upstream data pipeline issues)
  • Product Managers (to understand data needs for new features)
  • Business Analysts (for data definitions and reporting needs)
  • Junior Data Scientist Assistants (your mentees)
Outside the business
  • Data Vendors (occasionally, for data quality discussions)
  • Platform Providers (e.g., AWS, Snowflake support)

9What you need before you start

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

  • A minimum of 5 years of hands-on experience in a dedicated data preparation, data analysis, or data engineering role.
  • Proven ability to independently design, build, and maintain robust data pipelines using Python and SQL.
  • Demonstrable experience with advanced data wrangling techniques (e.g., complex joins, window functions, regex for text cleaning).
  • Experience mentoring junior team members or leading small technical workstreams.
  • A portfolio or demonstrable examples of complex data cleaning projects you've owned and delivered.
  • Strong understanding of version control (Git) and collaborative development workflows (e.g., Pull Requests).

10What to practise next

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

Advanced Data Modelling & Schema Design

As you own more complex data pipelines, you'll need to think beyond simple transformations. Understanding how to design efficient, scalable data models and schemas will be crucial for supporting future AI initiatives and ensuring data integrity.

Dimensional Modelling (Star/Snowflake Schemas) · Data Normalisation & Denormalisation · Data Lakehouse Architecture · Schema Evolution & Versioning

  • This week: Read up on Kimball's dimensional modelling techniques and their application.
  • This month: Analyse one of our existing analytical datasets. How would you redesign its schema for better performance or flexibility?
  • Month 2: Propose a new data model for an upcoming project, considering future analytical and AI needs.
  • Month 3: Get feedback on your proposed data model from a Data Architect or Lead Data Scientist.

Quick win: Take a critical look at the current data models you work with. Can you identify any inefficiencies or areas for improvement? Just asking the question is a start.

11Staying current once you are in

What people here do to keep up
  • Regularly contribute to open-source data projects or maintain a public GitHub repository showcasing your data wrangling and pipeline skills.
  • Attend industry conferences (e.g., PyData, ODSC) or local meetups to stay current with the latest tools and techniques in data science and AI.
  • Complete advanced online courses on data engineering, cloud data platforms, or specific machine learning topics that interest you (e.g., Coursera, Udemy).
  • Participate in Kaggle competitions or similar data challenges to hone your problem-solving and feature engineering skills.
  • Actively seek out opportunities to mentor junior colleagues or lead internal knowledge-sharing sessions on data best practices.
  • Read relevant academic papers or industry blogs to keep up-to-date with new research and trends in AI and data.

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.

Advanced Prompt Engineering & LLM Integration for Data Tasks

Truth is, competitors are already using large language models (LLMs) to draft complex SQL queries, generate synthetic data, or even suggest feature engineering ideas in minutes. Analysts who figure this out will outproduce peers significantly. This isn't future tech; it's happening now.

  • Context Windows & Token Limits
  • Temperature Settings for Data Tasks
  • RAG (Retrieval-Augmented Generation) Architectures
  • Output Validation & Hallucination Detection
  • Prompt Chaining for Complex Analysis

Cloud-Native Data Orchestration (Advanced)

As our data infrastructure moves further into the cloud, managing complex data pipelines requires more sophisticated orchestration tools. Knowing how to build and maintain these will be crucial for ensuring reliable data delivery at scale.

  • Directed Acyclic Graphs (DAGs)
  • Idempotency in Data Pipelines
  • Containerisation (Docker/Kubernetes basics)
  • Monitoring & Alerting for Data Quality
  • Cost Optimisation for Cloud Compute

13What you’ll use

What you’ll use

Skills this role draws on

Technical

  • Data Wrangling & Munging
  • Exploratory Data Analysis (EDA)
  • Feature Engineering
  • Model Validation Support
  • Data Visualization & Storytelling
  • A/B Test Analysis Support

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

    Data Analyst (Advanced) to Senior AI Data Scientist Assistant

    3-5 years as an Advanced Data Analyst

    Skills to master

    • Moving beyond just reporting to building robust, automated data pipelines
    • deep Python and SQL for data transformation
    • understanding of feature engineering concepts.

    You're ready to move on when

    • You're constantly asked to pull 'non-standard' data and clean it up for others.
    • You've started automating your own reports and data pulls with Python scripts.
    • You're the go-to person for complex SQL queries on your team.
    • You've shown a keen interest in how data feeds into machine learning models.
  2. 2

    Junior Data Engineer to Senior AI Data Scientist Assistant

    2-4 years as a Junior Data Engineer

    Skills to master

    • Shifting focus from infrastructure to data quality and feature engineering
    • developing a strong understanding of statistical concepts for data validation
    • improved communication with data scientists.

    You're ready to move on when

    • You're comfortable building and maintaining ETL pipelines, but want more involvement in the 'what' and 'why' of the data.
    • You enjoy diving deep into data anomalies and figuring out the root cause.
    • You're looking to apply your engineering skills closer to the analytical and modelling side of the business.
  3. 3

    Mid-Level Data Scientist to Senior AI Data Scientist Assistant

    2-3 years as a Mid-Level Data Scientist

    Skills to master

    • Deepening expertise in data wrangling and feature engineering, potentially specialising in a particular domain's data challenges
    • focusing on enabling other data scientists.

    You're ready to move on when

    • You find yourself spending most of your time on data preparation rather than model building, and you enjoy it.
    • You're passionate about data quality and want to make a significant impact on the foundational data layer.
    • You enjoy mentoring and supporting other data scientists with their data needs.

15Where this role leads

The long view: This work can lead to a role as a principal data assistant, a lead data scientist, a data operations manager, or a data architect.

The ten Future Fluencies

Zavmo analysis

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

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

The highlighted ones are the Fluencies your role leans on hardest, from how Senior AI Data Scientist Assistant is actually changing.

Where it leads

Roles this leads to

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

  • Lead Data Assistant / Associate Data Scientist (L4)

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 Practical Data Science, worked against Senior AI Data Scientist Assistant.

Practical Data Science Level 4

Applied to your work in Senior AI Data Scientist Assistant

The objective of this unit is to enable learners to apply statistical and machine learning techniques to solve data science problems. Learners will gain practical skills in regression analysis, forecasting, model creation and tuning, natural language processing, and data mining to extract valuable 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.

Start my Senior AI Data Scientist Assistant plan, free
See Your Progress Grow Illustration
Senior AI Data Scientist Assistant
  • Data Wrangling & Munging
  • Exploratory Data Analysis (EDA)
  • Feature Engineering
  • Model Validation Support
  • Data Visualization & Storytelling
  • A/B Test Analysis Support
This is your Mind Palace on learn.zavmo.ai. Every skill above comes from this role's own record, not an example borrowed from another job. A node lights up when you evidence it, and what you build stays yours between jobs. That is the part a course cannot do.

16The detail, folded away

Everything else the record holds

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

Where it leads next, rung by rung

Where it leads

The career path, and where it branches

Senior AI Data Scientist Assistant is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

  1. Lead Data Assistant / Associate Data Scientist (L4)

    2-4 years in current role

    One level up

    • Data Architecture Design Designing data schemas and pipelines for new, complex projects.
    • Advanced Feature Engineering Developing novel feature engineering techniques for cutting-edge AI models.
    • Model Deployment Support Assisting with the operationalisation of AI models, ensuring data feeds are robust.
    • Cross-Team Standardisation Defining best practices and tooling for data preparation across multiple teams.

  2. Data Scientist (L5)

    3-5 years in current role

    Two levels up (a significant jump)

    • End-to-End Model Development From problem definition to deployment and monitoring.
    • Advanced Machine Learning Deep learning, reinforcement learning, causal inference.
    • Model Interpretability Techniques to explain why an AI model makes certain predictions.
    • Experiment Design Designing rigorous A/B tests and other experiments to validate model impact.
Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, data preparation can be a grind. But what if you could cut down the tedious bits and focus on the really interesting challenges? Our AI Productivity Hub is designed to do just that, giving you superpowers to streamline your daily workflow.

As a Senior AI Data Scientist Assistant, you're constantly wrestling with messy data, complex queries, and endless debugging. Imagine having an intelligent co-pilot that helps you write code, debug faster, and even summarise your findings. That's exactly what our AI tools offer – they're not here to replace you, but to make you incredibly more efficient and effective.

AI-Powered Code Automation

Use tools like GitHub Copilot to auto-generate boilerplate code for common `pandas` operations, complex SQL queries, and visualisation setups. This drastically reduces the time you spend on repetitive coding tasks, letting you focus on the logic, not the typing. Think of it as having an expert pair-programmer constantly at your side.

Automated Exploratory Data Analysis (EDA)

Leverage libraries like `pandas-profiling` or `Sweetviz` to instantly generate comprehensive EDA reports on new datasets. These tools highlight distributions, correlations, and potential quality issues in minutes instead of hours. You'll get a quick, deep understanding of your data without writing a single line of manual plotting code, freeing you up for deeper dives.

Intelligent Debugging & Research

Paste obscure error messages or library-specific questions into an LLM (like ChatGPT or Claude) to get instant explanations, code examples, and links to relevant documentation. This bypasses slow traditional searching and gets you unstuck much faster. It's like having a senior engineer on call 24/7 to help you troubleshoot.

Smart Documentation & Summarisation

Use AI tools to automatically generate markdown documentation for your Python functions, summarise the key findings from a complex Jupyter Notebook, or rephrase technical explanations for a non-technical audience in Slack or email. This saves you valuable time on administrative tasks, ensuring your work is always well-understood and reproducible.

Common questions

Common questions

How do you become a Senior AI Data Scientist Assistant?

Common routes in include Data Analyst (Advanced) to Senior AI Data Scientist Assistant (3-5 years as an Advanced Data Analyst), Junior Data Engineer to Senior AI Data Scientist Assistant (2-4 years as a Junior Data Engineer) and Mid-Level Data Scientist to Senior AI Data Scientist Assistant (2-3 years as a Mid-Level Data Scientist). Times vary with prior experience.

Where can a Senior AI Data Scientist Assistant progress to?

This role can lead on to Lead Data Assistant / Associate Data Scientist (2-4 years in current role) and Data Scientist (3-5 years in current role), depending on the skills you build.

What new skills matter most for a Senior AI Data Scientist Assistant?

Increasingly, Advanced Prompt Engineering & LLM Integration for Data Tasks and Cloud-Native Data Orchestration (Advanced). 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

17Where to go from here

Other roles at Level 3

Same depth of qualification, different job. Useful if the work appeals but this particular role does not.
This job at other levels
More jobs in technology and data

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

The skills here transfer across most industries. Every organisation needs clean data and working pipelines to support its AI and analytics. This can lead to work in fintech, healthcare, e-commerce, or government, applying the same skills to different problems.

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 Senior AI Data Scientist Assistant 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.