The Multiverse blog

Hillingdon Council is launching a new Digital Excellence Academy for 75 staff members, as part of a specialist upskilling drive. The project will help the council execute a new digital strategy to better integrate services and improve quality of life for the community.

By building its digital capabilities, the council is looking to empower colleagues to improve their efficiency with digital and data while utilising these new skills to inform better decisions for residents and enable financial sustainability.

Training is being delivered by Multiverse, a tech company that identifies, closes and prevents skills gaps, through personalised, on-the-job learning. Multiverse has trained more than 16,000 apprentices in AI, data and digital skills since 2016.

Hillingdon Council’s new Digital Excellence Academy will see 75 employees enrol onto programmes such as ‘Data & Insights for Business Decisions’, a Level 3 apprenticeship which covers core technical skills including cleaning, formatting and preparing data. ‘Transformative Leadership’, a Level 5 course, is designed to help employees build strong leadership foundations, manage high-performing teams and drive continuous improvement.

Multiverse’s Skills Intelligence Report found that more than a quarter of local government and council employees’ time spent on data-related tasks could be more efficient if skills were enhanced. Hillingdon Council is ensuring it is ahead of the sector status quo by making significant steps to enhance the digital skills of employees.

Matthew Wallbridge, Chief Operating Officer of Hillingdon Council said:“Our Digital Strategy sets out our vision to embrace technology to be more efficient and make it easier for residents to use council services, including supporting those who are unable to use technology. Upskilling our teams in digital and data is a clear demonstration of our commitment to this, and in driving positive outcomes for our community.”

Multiverse combines work and learning to unlock economic opportunity for everyone. It works with more than 1,500 organisations to close critical skill gaps in the workforce in AI, data and tech, through a new kind of apprenticeship.

Gary Eimerman, Chief Learning Officer at Multiverse, said: “We’re confident that Hillingdon Council’s Excellence Academywill both improve its services and create a culture of continuous learning, driving innovation within the organisation. Staff will be better equipped to understand and use data, leading to more effective and efficient service delivery.”

Why does this matter? From the perspective of individual professionals, it means opportunity abounds to unlock a rewarding, lucrative career by harnessing the right new skills.

Individuals who engage in digital upskilling tend to earn more, experience increased job satisfaction, and progress further in their preferred career paths. Apart from these benefits, upskilling is becoming increasingly crucial to remain competitive in the job market throughout the next decade and beyond.

Interested in learning how you can future-proof your career without quitting your job? Then read on. Below, we'll cover the following:

• What is upskilling?
• Why is upskilling important?
• Benefits of upskilling
• How to Upskill in 2024
• Upskill with Multiverse and future-proof your career

What is upskilling?

Upskilling is the process of improving your professional skill set. It typically refers to the training involved when mastering new skills and technologies, such as tools, coding languages, or frameworks. The ultimate goal of upskilling is to broaden the depth of knowledge of your work, allowing you to take on more responsibilities and pursue career advancement opportunities.

Upskillers come from all ages and industries. They're usually professionals who want to progress in their current careers through continuous learning. Upskilling can be undertaken via structured learning on the job, studying and practicing on your own time, or a combination of the two.

Why is upskilling important?

Many companies undertake the task of training employees via dedicated upskilling programmes with the goal of developing more productive, innovative teams. But upskilling can also help employees themselves unlock significant economic and career benefits.

75% of employees who participate in an upskilling programme with Multiverse saw a salary increase since starting the programme.

Furthermore, research by The Department for Culture, Media and Sport (DCMS) shows upskilling is essential for job security. With 48% of UK businesses "recruiting for roles that require hard data skills,” the demand for employees with data skills is strong. Of the 48% of businesses surveyed, 46% have struggled to fill these roles over the last two years.

In other words: There aren't qualified workers to satisfy the spiking demand for next-generation digital skills at work.

Upskilling isn't just crucial for job security now. It will also help you future-proof your career over the next decade and beyond.

Take the fields of data analysis and AI as an example. DCMS data suggests the demand for data analysis will increase 33% by 2026. Meanwhile, Multiverse research suggests 81% of tech firm leaders expect to increase their investments in AI over the next three years.

Upskilling vs. reskilling: Understanding the difference

While both upskilling and reskilling aim to address professional skills gaps, they serve different purposes in professional growth:

• Upskilling: Focuses on enhancing and expanding existing skills within your current field to take on more advanced roles. For example, a marketing professional might upskill by learning advanced data analytics tools to create more impactful campaigns.
• Reskilling: Involves acquiring an entirely new set of skills to transition into a different role or industry. For instance, a retail worker might reskill to become a software developer by learning coding and programming languages.

Both strategies are critical for adapting to changes in the job market, whether you're looking to climb the ladder in your current career or pivot to a new one. Identifying your unique skill gaps will help you determine whether upskilling or reskilling is the right path for your goals.

For more information on whether upskilling or reskilling is right for you, check out our companion blog on the topic.

Benefits of upskilling

Those who upskill have the potential to earn more, excel in their career, and learn durable skills that can be applied on the job. Upskilling can future-proof your career, leading to greater job security, continuity, and satisfaction. Let's take a closer look.

Increase your income

According to AND Digital, more than a quarter of UK employees didn't receive promotions due to a lack of digital skills, and 10% missed a pay increase for the same reason. This highlights the importance of having the right skills for career advancement and earning potential.

1 in 3 professionals who upskill through Multiverse either received a promotion whilst on programme or within 6 months post-programme. Additionally, according to Multiverse’s ROI of AI report, 56% of workers receiving AI training are likely to negotiate for higher pay in the next 12 months.

Investing in your skills can directly impact your career progression and earning potential. You don't have to climb the career ladder in a traditional sense (i.e., advancing to management) to upskill and increase your salary. You can become a master in your field, progress to a senior specialist, and then command more money for your skill level.

As an example, consider the impact of seniority on Data Analyst salaries. The median Data Analyst in the UK is around £37,000. But Directors of Data Analytics with advanced skills and experience earn £95,000 on average before other incentives and bonuses. (Source: Glassdoor).

Experience job satisfaction

When you upskill, you can create meaning in your work. The increased purpose at work leads to higher engagement. According to Gallup, engaged employees are less likely to switch jobs, which indicates job satisfaction.

Job satisfaction is even likelier if you upskill in highly relevant areas to your existing role. For example, employees who feel their skills are irrelevant are ten times more likely to start job hunting. In this case, upskilling won't just mean more job satisfaction but more job continuity.

Increase your job security

As many as 85% of all jobs available in 2030 have yet to be invented, meaning they'll demand new skills. The future aside, 61% of businesses already feel limited "by an insufficient digital vision and strategy."

You need to upskill if you want to meet the existing demand and future-proof your career. Digital skills like data analysis and AI will be more in demand. Whether you upskill in these areas or in general, you'll likely experience more job security.

How to upskill in 2025

From choosing a goal to upskilling for free without quitting your job, here are five ways to advance your career in 2024.

Pick a goal

When upskilling, first define what you want from your work and life. Do you want a career that matches your interests? A role that offers work-life balance? Maybe you have a specific salary range in mind because you want to start a family.

Consider your answers carefully and use them to pick your goal. With your end goal in mind, you can plan and prioritise the steps needed. Plus, when you understand why you're upskilling in the first place, it's easier to stay focused — even if things get challenging.

Example:

Let's say your goal is to get a pay rise. You're currently a Junior Data Analyst. If you increase your skills, you'll be in a solid position to get a Senior Data Analyst role. That progression comes with a salary increase.

You choose to upskill through an Advanced Data Fellowship programme, which you can do alongside your work. You can get your promotion mid-way through the programme and meet your goal of a pay rise.

Research jobs of interest to you

If you know you want to upskill but need to clarify your end goal, do your research. Start with a brainstorming session and think about job titles, roles, or progression routes that appeal to you.

If you're unsure where to start, check out different career options for Upskillers and narrow down based on your interests. You can then research existing professionals in those fields through platforms like LinkedIn. That will give you an idea of what upskilling steps you need to take.

Example:

It's been a few years since you progressed from a Junior to a Senior Data Analyst. Your new career goal is a different role in a similar field. You remember a former colleague who pivoted from a Senior Data Analyst to a Business Analyst position.

The idea has always stuck in your mind, but you need help figuring out how to switch roles. You head to LinkedIn to revisit your old colleague's professional profile. You then double-check the skills and certifications they have that are relevant to their Business Analyst role.

Chat with someone in the field

One of the fastest ways to understand something is to talk with someone with real-life experience in that area. In the case of upskilling, this involves finding a mentor you can chat to about your next steps.

When you find a mentor, you can ask them how they went from A to B and what specific skills they need for their current role. If you don't have an existing colleague or professional in mind, consider joining professional communities or groups. You can find these groups on LinkedIn for free or join paid Professional Slack communities.

Example:

As a Senior Data Analyst considering a pivot into Business Analysis, you reach out to your old colleague through LinkedIn. You meet for a virtual coffee and learn how they pivoted from data analysis to business analysis. They upskilled with a Business Analyst apprenticeship and had a new role with higher pay in just over a year.

Start a project

A real-world project will help you practice and develop skills to meet your career goals. You could start a personal project in your own time or volunteer for a relevant project at work.

If you choose the latter, consider speaking to your Line Manager about your upskilling goals. They might have a relevant project you can get started on straight away. Doing this also keeps you top of mind for progression opportunities that match your career goals.

Example:

After researching your colleagues' professional skills and speaking to them directly, you're sure you want to become a business analyst. You have enough data analysis experience but never had to present your findings to stakeholders.

Your colleague mentioned that presentation skills are a critical difference between data and business analysts. So, you set yourself a personal project to practice your presentation skills. You decide to focus on presenting your data insights in a way that shows real-world business potential.

Upskill at no cost with Multiverse without quitting your job

Future-proof your career with Multiverse’s upskilling programme. This programme provides a structured curriculum and personalised coaching to help you achieve your unique career goals. You’ll develop versatile and transferable skills that enable you to adapt to changes in your industry.

Unlike other programmes, the upskilling certification has no opportunity costs. Our on-the-job training programme is designed to fit the schedules of busy working professionals so you can keep working full-time while you learn. It’s also totally free with no strings attached.

Upskill with Multiverse and future-proof your career

There’s never been a better time to upskill for the future. Emerging technologies like artificial intelligence, big data, and cloud computing are reshaping industries and driving innovation. Learning how to use these cutting-edge tools can help you keep up with the ever-changing digital landscape and advance your career.

Multiverse’s upskilling programmes are the ideal way to upgrade your toolkit without sacrificing your current career for costly formal education. We offer training in critical skills like advanced analytics and technology consulting. Our programmes will help you enhance efficiency, gain marketable skills, and save money. Get in touch today to learn more.

We’re ambitious about the impact we want to create, and a key part of this is Multiverser’s development. With competency mastery at our core, we recently refreshed how we think about Product Management as a craft. Today, we’re sharing our new internal framework for Product Managers (PMs) at every level at Multiverse.

Our goal for the framework is that it’s fair, actionable, and measurable. It should create a pathway for great Product Management through clear expectations, speak to every aspect of the product development lifecycle, and help PMs—and Multiverse—grow.

The evolution of Product Management at Multiverse

Our team has experience across the spectrum—from seed-stage startups to public companies, including Google, Spotify, Meta, Hubspot, and more. With this wealth of knowledge, we initially adopted a simple way to understand Product Management: by asking four key questions (4Q) that focused on impact, speed, product expertise, and leadership.

1. Are you achieving impact? It all comes down to this. You need to build software our users want and love.
2. Do you ship at pace? We’re a scale-up and still have a lot to build. We need to be nimble and build fast.
3. Are you the expert on our user and product? The more you understand user needs and the product, the higher the chance of building the right tools and features for impact.
4. Are you leading and motivating your team, stakeholders, and organisation? If everyone shares the same mental model of the user and business goals and is excited about what you are building, the faster you can ship.

This simple rubric served us well during our rapid growth. It helped ensure that PMs delivered for users, shipped at a pace, and led their teams effectively. However, as Multiverse grew to 800+ employees and our product team matured, we needed something more robust.

Introducing our new competency framework

Our new framework uses the Driver Tree approach—a more dynamic, outcome-focused way to align every PM’s growth with business impact, rooted in the original intentions behind the 4Q approach. The Driver Tree links each category of competencies— shipping, mastery, and leadership—directly to role-specific expectations, making it clear how PMs at each level can create user value by driving the product's and the business's success.

The framework avoids ambiguity by detailing the skills required at every role level. As PMs progress in their careers, they take on greater responsibility and more complex problem spaces. The framework will help them navigate this progression by clearly defining what success looks like at each stage. PMs can assess their standing and provide clear, concrete examples of what is expected at each level.

Try the framework yourself

We’re excited to open up this framework for anyone to use. Take a look at it here, make a copy, and feel free to adapt it to your teams. As Multiverse continues to grow and evolve, so will this framework. We welcome any feedback and look forward to hearing how it works for you.

ERGs are groups created for employees and led by people who share common life experiences or identities. Typically, these groups are comprised of people within communities that have been historically and systemically marginalised.

ERGs provide support and safe space for underrepresented and/or marginalised communities. They help employees in their professional development, increase engagement and retention, and drive towards organisation-wide goals. Disabilities @ Multiverse is a community for those who identify as having an apparent, non-apparent, short term, long term disability and/or identify as neurodivergent. Our ERG is open to all employees who are passionate about creating an inclusive workplace for individuals with disabilities.

We sat down with Meelie Thorpe, Apprentice Support Executive based in our London and the new Disabilities @ Multiverse Chair, to talk about her new role.

What inspired you to run for Chair of Disabilities @ Multiverse?

My incredible colleagues who form the heart and soul of Multiverse inspired me to run for Chair. I am continually excited by the idea of creating a vibrant space where we can all share, connect, and enjoy the company of people who experience the world in a similar way. For me, it's essential that this community encourages connection and sharing, but also advocacy and learning. I’m passionate about having a tangible, positive impact on our employee experience!

What is the main goal of your ERG?

My main goal of the ERG is to cultivate an inclusive and empowering environment for employees with disabilities and allies, promoting awareness, support, and career growth opportunities within the company. Our aim is to enhance workplace experiences and create an equitable and inclusive community where disability can be celebrated! Conscious inclusion is at the heart of everything we do.

Why do you think the ERG is important?

For me, it is important to make meaningful contributions to creating an equitable and inclusive experience for all employees. The barriers that people with disabilities face in society mean that often we do not have the same opportunities as our peers. The ERG is an important step towards bridging that gap, and creating a safe space for members to contribute to the growth and development of employee experiences at Multiverse.

What are you proud of achieving so far?

So far we’ve already had amazing opportunities to share through our quarterly events and monthly meetings.

In September we put on an internal roundtable titled: Voices of Strength: Creating Accessible Spaces at Multiverse. Which we put on to provide a platform for employees with disabilities to share their experiences, insights, and discuss key topics related to workplace inclusion, accessibility, and support.

And in celebration of International Day of Persons with Disabilities in December, we’re putting on a series of events:

• A neurodiversity champion workshop.
• An inclusive manager workshop.
• And a session on making our product (even) more accessible.

If our commitment to diversity inspires you and you wish to be a part of a company that invests genuinely in its people, we would love to have you on board. See live roles.

As many as 25 days a year are lost to data skills gaps according to Multiverse research.

A skills matrix can help map these gaps and give leaders direction on what action to take.

In this article, we’ll explore what is a skills matrix, how to use one, and best practices for creating your own.

What is a skills matrix?

A skills matrix maps employees’ skills onto a grid with each person rated for proficiency, helping leaders understand how well different teams or a whole workforce can perform specific tasks.

The exercise helps identify skills gaps and growth opportunities for current employees while acting as a roadmap for a wider skills strategy.

A skills matrix is different from a competencies matrix. A competency matrix looks broadly at attitudes, knowledge, and behaviours, whereas a skills matrix focuses purely on skills.

What is a skills matrix used for?

An estimated 11% of the working week is lost to data skills gaps, according to the Multiverse Skills Intelligence Report. But by mapping the gaps using a skills matrix business leaders can:

• Create well-rounded teams with complementary skills – putting the right people in the right place.
• Track progress in skill acquisition over time, with success measured against business goals.
• Support the case for employee training programmes and upskilling.

How to use a skills matrix

A skills matrix is based on data, helping you make better decisions about placing the right people on the right projects.

If information is lacking – for instance when a company has no central record of the skills available – it’s harder to make the best choices.

So, alongside a skills matrix, the best practice is to build a skills inventory – which digitally documents all the capabilities in a company.

Using this foundation, a skills matrix can assess the strengths and weaknesses of individuals, teams or departments – scored against company goals.

By comparing available skills, it helps you make informed decisions about employee training, resource allocation and hiring.

And because it’s grounded in data, you reduce the need to make skilling decisions based on guesswork or industry trends.

Skills matrix example – free template to use

Below is an example of a skills gap matrix – mapping out the desired skills with scoring for where an employee is today and the ambition for the future.

What should be included in a skills matrix?

When used as part of a skills gap analysis, an effective skills matrix will bring together several elements beyond the table of desired skills scored against a proficiency scale.

Here are five best practice points for using a skill matrix:

1. Create a skill taxonomy: Follow a structured list of skills defined across all departments, as part of a skills inventory. Standardising the language makes it easier to measure and track progress.
2. Calibrate skills matrix proficiency scales: Make sure these reflect business needs and with clear progression across different levels. This ensures consistency when used across the business.
3. Integrate with HRIS (HR information systems) and skills assessment tools: Make your skills matrix a live document, allowing for continuous monitoring with updates made in one central place – helping you track progress and prove ROI.
4. Workforce planning: Use predictive analytics to assess future skill needs with strategic workforce planning. Identify opportunities for upskilling/retraining the existing workforce and ensure efforts create immediate business impact, not just provide an employee benefit.
5. Foster a culture of continuous skill development: Encourage employees to take a stake in their progression while showing how they can make an impact on business goals.

Using a skills matrix to prevent skills gaps

When demand for AI and data skills is growing, leaders looking to the future can benefit from mapping their current skills gaps using a skills matrix.

The time to act is now: 90% of employees want to improve their data skills, according to the Multiverse Skills Intelligence Report

Leading built environment consultant, Rider Levett Bucknall (RLB) UK & Europe has launched a Data & AI Transformation Academy, designed to digitally upskill its team and leverage innovative data solutions, providing an enhanced service offering to clients.

Established to reinforce RLB’s positioning as a leader in data-driven construction consultancy by harnessing the power of data analytics and research, this academy plans to offer a continuous learning programme to RLB employees across the UK and Europe. RLB will look to launch additional cohorts every six months to maximise impact for clients in the face of rapidly changing technology.

RLB has partnered with tech leader, Multiverse, which provides personalised, on-the-job learning. RLB’s academy features programmes like the 13-month ‘AI for Business Value’ programme, and the Data Fellowship, a level 4 apprenticeship programme. Learners will gain hands-on training in AI implementation and business intelligence tools, fostering a future-ready, data fluent culture, with apprentices taking part from teams including cost management, sustainability, built asset consultancy and project management.

Matt Sharp, Chief Digital Officer at RLB said: “This academy lays the foundation for transforming the skills of our team and the service offering to our clients. As our business, projects, clients, and industry undergo significant digital and data transformation, it is crucial that we equip our people with the necessary digital and data skills. With more data at our disposal, RLB needs individuals who can unlock its potential and tell compelling stories through data.”

Susan Nelson, Digital Change and Adoption Manager at RLB said: “By launching this programme, we’re building a data-fluent culture. This will drive digital innovation and agility, preparing our team to shape the future of construction and property management consultancy.”

Gary Eimerman, Chief Learning Officer at Multiverse said: “RLB is cementing its position as a sector pioneer by equipping employees with vital business efficiency tools. Our research shows that the construction industry struggles with a data skills gap. Closing this will have a profound impact, accelerating project timelines and reducing errors; even helping to better measure and improve the sustainability of building projects.”

Skills are viewed as crucial to future business success. According to Multiverse research, more than two thirds (69%) of leaders believe their organisation will need different workforce skills to stay competitive by 2030.

It’s no surprise that businesses are upping their investment in learning as a result, with some 77% of leaders predicting their learning and development budget will increase by 2030.

Artificial intelligence (AI) is another driving force. As leaders push their organisations to adopt AI, workforce skills shortages are slowing progress – or even stopping AI initiatives in their tracks entirely. Tech leaders name skills gaps as a top blocker to AI implementation, with half of organisations planning to plug the skills gap with training as a result.

With these forces already in motion, HR leaders will face growing demand to show proof that employee training initiatives are delivering real business impact – and measurable ROI.

In this article, we will explore five simple steps for calculating the return on investment of employee training, helping you measure impact and make the case for future spending.

Understanding ROI in the context of employee training

ROI is a financial metric that measures the profitability of an investment by comparing the net gain or loss to its initial cost. A ROI calculation helps business and HR leaders evaluate the effectiveness and value of learning and development initiatives.

As well as validating the use of training time and budget, calculating ROI helps businesses to monitor the effectiveness of training programmes, and connect training with wider business goals.

Step 1: Set clear employee training objectives

Before launching any new employee training or upskilling initiative, clearly define what you want to achieve, and the metrics to measure success.

Wherever possible, align all learning programmes with strategic priorities and business outcomes. For example, an outcome could be: implementing an AI strategy, boosting productivity, or improving employee retention.

Next, assess the barriers that prevent you from achieving these aims, such as low employee satisfaction, low adoption rates for new AI tools, or high turnover rates for data specialists.

Then, agree on the metrics you will track to measure changes to these outcomes. Increasing productivity, for example, can be measured by looking at output per employee, the time saved on tasks, or quality improvements.

If addressing specific skills gaps, such as the AI example mentioned above, conducting pre- and post-training employee skill assessments can help you further benchmark and measure success.

Step 2: Work out the costs of employee training

Tallying up your costs will come from two different sources. Firstly, direct costs include the payment for any training materials, an external trainer or supplier if you used one, and any venue or equipment charges.

Secondly, indirect costs come from the time employees spend away from their daily tasks to complete the training.

Step 3: Map the tangible and intangible benefits of employee training

Here, the benefits split into two categories:

• Tangible benefits include productivity gains, employee retention improvements, and any cost savings created by employees using their new skills from the training programme.
• Examples of intangible benefits are enhanced collaboration, improved confidence, and an increased potential for innovation – which can be measured through methods like employee surveys or testimonials. Keep these in mind for reporting back (see step five).

Step 4: Run your ROI calculation

When going through this exercise, consider how to balance any short-term costs with the long-term benefits. A typical ROI formula looks like this:

ROI = (Benefits of employee training, minus costs) divided by costs x 100

For the clearest ROI figure, include the metrics which can be quantified. Usually, this is monetary: it can be measured against both the inputs of time and cost, as well as the outputs, such as increases in revenue and productivity.

Step 5: Tell a story when reporting results

Now it’s time to communicate the impact. After running the ROI calculation, don’t forget about the intangible benefits you recognised: they form a crucial part of the story to tell.

Make sure your metrics and objectives line up with the business objectives, visualise your data to make it more digestible, and include qualitative data such as employee testimonials to add colour.

Be transparent about any challenges to help inform future plans. What did you learn, and what could change next time to improve ROI?

Case study example: Jaguar Land Rover

Let's look at how Jaguar Land Rover measured the impact of its upskilling efforts. The company had three objectives:

• Equip employees with the knowledge and skills needed to identify opportunities to reduce costs and increase revenue.
• Build a data-driven culture that knows how to apply and leverage data in various scenarios – with the goal of increasing productivity and efficiency across the business.
• Increase employee satisfaction and engagement by investing in skills development.

Jaguar Land Rover launched the Multiverse Data Fellowship programme to equip employees with the skills needed to become experts in data analysis, modelling and machine learning. Currently, there are 600 employees in the programme across every department.

After assessing the ROI of the employee training programme, JLR found that learners had:

• Identified new opportunities to increase efficiency
• Increased productivity
• Increased employee satisfaction

Read the full story here.

Linking ROI with the bigger picture

Calculating the ROI of employee training is not just a financial exercise – it's a powerful way of advocating for further training initiatives.

By showing the real benefits of past training, HR leaders can make a compelling case for additional investments in employee development – especially given today's urgent demand for workforce AI skills.

Get in touch about your skills transformation today.

Super-intelligent humanoid robots aren’t roaming the streets (at least, not yet). AI technology, however, is transforming industries and reimagining the way businesses operate. For workers, it’s also an inflection point — one that’s sparking a reevaluation of the skills needed to achieve staying power in a challenging job market. By 2027, an estimated 42% of companies surveyed by the World Economic Forum will prioritise training workers in AI and big data skills.

As AI becomes more integral to everyday operations, businesses need skilled workers to develop, train, and apply this technology. 81% of tech leaders plan to increase their investments in AI over the next three years.

This poses a massive opportunity for forward-thinking professionals to take charge of their career trajectory by learning high-value AI skills. According to a Multiverse report, 56% of surveyed workers at AI-integrated organisations plan to negotiate higher pay in the next 12 months.

Below, we’ll take a deep dive into the crucial AI skills and tools needed to thrive in the AI-enabled job market — both today and tomorrow. We'll also share practical tips and resources for expanding knowledge in these areas.

Understanding the LLM revolution

AI adoption has skyrocketed as organisations race to stay ahead of the competition. According to consulting firm McKinsey & Company, the percentage of businesses using AI tools jumped from 55% in 2023 to 72% in 2024. With 50% of organisations already using AI for two or more business functions, it’s clear that this isn’t just a momentary trend; it’s a seismic shift in how we work.

The versatility of AI tools has significantly contributed to their surging popularity. Many businesses rely on this technology to automate repetitive or time-consuming workflows. In the healthcare industry, for instance, professionals are using AI to automate document classification, patient indexing, and other data entry tasks. Meanwhile, marketing and sales teams are turning to AI to hyper-personalise content and automatically send follow-up emails to prospects.

Beyond automation, businesses across industries are employing AI to analyse data and make more strategic decisions. For example, Schneider Electric’s Sustainability Business uses AI-assisted forecasting tools to predict extreme weather events. As Schneider Electric Sustainability President Steve Wilhite explains, “These forecasts, partnered with human-expertise, will support everything from energy efficiency and optimisation to emissions reduction to grid resiliency.”

Despite the massive gains, even the most AI-savvy businesses have struggled to unlock the technology’s full potential. Only 27% of business leaders consider their organisations “AI Adept,” which means they’ve embedded AI across their operations to improve strategic decision-making.

“All organisations should strive to be AI native — fully embedding and realising the ROI advantages of AI — in the years ahead,” explains Anna Wang, Head of AI at Multiverse. “However, because of the newness of the technology and the pace of change, many organisations are struggling to get a clear view of their own progress.”

For workers and organisations, understanding the tools and technologies leading the AI revolution — and how to leverage them to drive demonstrable business value — is essential. Below, we’ll highlight three key technologies gaining traction in workplaces in the UK and beyond.

ChatGPT

In November 2022, OpenAI launched ChatGPT, an AI-powered conversational model that quickly became a household name. By August 2024, the company estimated that an astonishing 200 million people were using the tool weekly.

ChatGPT is a large language model (LLM) trained on vast amounts of data. Developers create LLMs using neural networks that contain interconnected nodes and layers. These structures learn how to process and transmit data, just like neurons in the human brain.

ChatGPT’s neural networks use natural language processing to understand and respond to human language. The LLM breaks down text into patterns and smaller components, analysing it for meaning and context. It uses this information to generate relevant responses that closely mimic human writing or speech.

The conversational nature of ChatGPT makes it incredibly accessible, contributing to its widespread popularity. According to the ROI of AI report, 61% of workers have picked up new AI skills by experimenting with ChatGPT.

Text-To-Image Generators

Generative AI tools like MidJourney and DALL·E 3 use advanced machine learning techniques to create images from text prompts. Unlike ChatGPT, which is powered by LLMs designed for text generation, these platforms rely on diffusion models or other image-generation architectures. Diffusion models work by adding “noise” (random pixels) to data, such as images, and then gradually removing noise through multiple iterations to generate new images based on the text prompt.

Businesses can use image-to-text generators like Midjourney to generate personalised images in a fraction of the time it takes to create traditional art. This technology also helps professionals brainstorm new content ideas, such as film posters and social media graphics.

London-based ad agency 10 Days is one company that has embraced text-to-image generators. Their creative team uses these tools to design visually complex brand characters, logos, packaging, and picture books.

GitHub Copilot

GitHub Copilot is an AI-powered programming assistant built on an LLM. It allows users to input code snippets and generates suggestions to complete them. The software also answers coding-related questions, detects bugs, translates code into different programming languages, and more.

According to Stack Overflow’s 2024 Developer Survey, 44.2% of professional developers use GitHub Copilot for programming tasks. This tool lets professionals write code more quickly and accurately, significantly improving efficiency. A GitHub study found that developers who used Copilot completed coding tasks 55% faster than those who didn’t use this tool.

According to payments startup Pockyt founder Mason Lin, this achievement is only the beginning of a larger digital transformation for the startup.

“We anticipate a 500% increase in productivity in the medium to long term as we continue adapting AI and fine-tuning our software development life cycle,” Lin says.

AI and job growth: The path to a resilient career

As more businesses embrace AI, many professionals are understandably curious about how it will affect their careers. While it may take decades to understand the full impact of these advancements, one thing is certain: AI is fundamentally reshaping the workforce.

By 2030, generative AI and similar technologies may automate up to 30% of current working hours. This shift could require up to 12 million Europeans to transition into new roles — twice the pre-pandemic rate.

But it’s not all doom and gloom. AI is unlocking exciting new job opportunities across all sectors. A 2024 Gartner survey revealed that 67% of mature organisations are developing positions related to generative AI.

The UK job market already reflects the growing influence of AI. According to a PwC report, AI-related job postings have increased 3.6 times faster than other positions. The report also found that UK employers are willing to pay a 14% wage premium for workers with AI skills — a clear indication of the value of skilled human operations in workplaces increasingly fueled by AI-driven insights.

“Skilled people are crucial to realising the full value of AI,” explains Gary Eimerman, Multiverse’s Chief Learning Officer. “Without a thorough understanding of AI, businesses may be limiting the value derived from the technology in the long-term.”

The UK government has developed several initiatives to support AI skill development. For instance, the Digital Skills Council offers resources to help workers reskill and upskill for digital careers. Additionally, the Secretary of State recently appointed tech entrepreneur Matt Clifford to spearhead the AI Opportunities Action Plan. This project will outline strategies to develop AI talent in the private and public sectors.

Expanding your AI skills now will help you get ahead of the curve and navigate the coming technological disruptions. Upskilling can also prepare you for emerging AI careers, such as:

Digital Transformation Consultant

A Digital Transformation Consultant helps businesses use AI and other technologies to automate workflows and drive innovation. They assess each client’s existing tech stack and develop a strategic plan for integrating new technologies.

Salary data:

• Entry-level: £41K
• Average: £60K
• High earners: up to £74K

Source: Glassdoor

AI Compliance Officer

An AI Compliance Officer oversees their organisation to make sure all employees use AI tools and data ethically and legally. They develop policies for AI usage, educate workers about best practices, and audit AI systems.

Salary data:

• Entry-level: £27K
• Average: £33K
• High earners: up to £50K

Source: Talent.com

Automation Consultant

An Automation Consultant identifies opportunities to develop more efficient and streamlined operations. They use AI software and other tools to automate workflows, from sending appointment reminders to ordering supplies.

Salary data:

• Entry-level: £35K
• Average: £47K
• High earners: up to £63K

Source: Glassdoor

Future-proofing your career: The 5 skills you need to stay relevant in the age of AI

The rapid adoption of AI technologies has led to a nationwide talent shortage. In 2024, 81% of UK IT managers agree that there’s a critical AI skills gap, an increase of 9% from the previous year.

A lack of education and few opportunities for hands-on practice have contributed to this growing skills gap. According to Multiverse data, only 45% of employees received formal training from their employers.

“Workers are fending for themselves, either funding their own AI training or learning through trial and error,” Wang says. As a result, “it is difficult for them to self-assess their own knowledge gaps and learn most efficiently with their limited time.”

Fortunately, there are plenty of resources to help you learn AI concepts and expand your digital toolbox. Here are the most critical skills needed for success in the AI-enabled workplace.

1. Prompt engineering

Prompt engineering involves writing and refining specific inputs to get more accurate and tailored outputs from generative AI tools. According to Multiverse data, only 14% of tech leaders believe their organisation lacks this skill — a testament to the primacy of prompt engineering in the hierarchy of foundational AI skills.

Whether using ChatGPT or other generative text tools, workers can deploy numerous strategies for writing effective AI prompts. These include:

• Providing necessary context, such as your desired tone, industry, and target audience
• Using roleplay prompts, like “Imagine you’re an experienced UX Designer who wants to create user-friendly features for a mobile app”
• Giving the AI a list of “do’s” and “don’ts”
• Inputting an example and ask the AI tool to model its response after it
• Experimenting with different versions of the prompt until you get the desired output

Prompt engineering allows professionals to generate more engaging and precise content. For example, a Data Analyst could use prompt engineering to create a detailed report highlighting actionable insights based on specific findings. Meanwhile, a Software Developer could prompt an AI tool to review code output for a new feature.

Taking an online course in natural language processing can help you learn how to develop better prompts. Experimenting with free tools like OpenAI’s Playground will also sharpen your skills.

2. Data analysis

Employees spend an average of 14.31 hours weekly — over 30% of their time at work — on data tasks.  Yet Multiverse survey data found data analytics to be the biggest AI skill gap organisations face, with 52% of tech leaders and workers agreeing their businesses are lacking in this area.

You don't need a degree in data science to learn this skill. Accessible AI-powered tools like Microsoft Power BI and Tableau make collecting, processing, and analysing data easier than ever. They also allow users to create engaging data visualisations and reports.

Let’s say a Marketing Specialist wants to improve their social media campaigns. They could use Tableau to collect engagement data from Instagram and analyse it for trends. For example, they may discover that videos with music consistently perform better than posts with photographs. Based on this insight, they can create similar content to capture their audience’s attention more effectively.

Tableau and Power BI offer many free resources — including tutorials and community forums — to help you strengthen your data analytics skills.

3. Identifying AI workflows and implementing AI-related projects

While there are many useful AI tools, it can be difficult to weave them into your company’s existing workflows. Multiverse data shows 48% of tech leaders think their organisation can’t execute AI projects effectively.

Creating a project roadmap will help you spot opportunities and implement AI tools successfully. This framework should include these steps:

• Assess existing workflows to understand your organisation’s challenges and needs
• Identify areas where AI tools can provide advantages over conventional methods
• Form cross-functional teams that bridge data science, software development, and other departments
• Create clear guidelines for AI usage and data governance
• Rollout AI systems gradually to prevent disruptions
• Establish performance metrics to make sure your project is on the right track

4. Building AI features

Developing AI features is another critical skill gap identified by 26% of tech leaders and workers, according to the Multiverse ROI of AI report. This skill requires a basic understanding of machine learning algorithms and data analytics.

Luckily, you don’t have to start from scratch while creating AI features. Tools like PyTorch and TensorFlow offer libraries and extensions that simplify the process of building and training machine learning models. Software developers can use TensorFlow’s LiteRT library to integrate machine learning models into Android and iOS applications.

You don’t need a software engineering background to contribute to these projects. Low-code platforms like Microsoft Power Apps have intuitive, user-friendly tools that anyone can use to build AI features.

5. Ethics and responsible AI usage

The vast majority (93%) of workers surveyed by Multiverse believe they use AI ethically. But AI technology raises many ethical and legal challenges that aren’t always immediately apparent.

A 2024 study by the University of Essex discovered that AI hiring systems can “create algorithmic bias against women” by filtering applications based on gendered language. Along with bias, data privacy is another significant concern. Notably, Google faced a class-action lawsuit for using patient data from the Royal Free NHS Trust without consent to train its AI models.

AI ethics frameworks can help you navigate tricky situations and adhere to data privacy laws. The UK government has developed ten principles to guide the ethical use of generative AI, prioritising accountability, human control, and transparency. Similarly, the European Union created a human-centric framework for ethical AI usage.

Improving AI skills: Challenges and opportunities

Strengthening your AI skills takes effort, time, and the willingness to step outside your comfort zone. Taking advantage of online resources and seeking guidance from mentors will help you navigate the learning curve. Here are three options for levelling up your AI skills.

Completing self-taught courses

Many organisations have developed online courses that let you study artificial intelligence at your own pace. For example, AWS and Coursera offer classes on machine learning, natural language processing, and other AI fundamentals.

These courses are a convenient way to learn foundational AI skills while focusing on the areas most relevant to your professional development. However, they typically don’t offer personalised coaching or opportunities for hands-on practice.

Going back to school full-time

Some upskillers return to university to earn degrees in artificial intelligence, computer science, information technology, and related fields. These programmes have structured curricula and may offer experiential learning opportunities like group projects and internships.

But the cost of going back to school can be high. College students in England pay up to £9,250 annually in tuition, plus living expenses and other fees. And that’s not factoring in the lost wages from time spent studying instead of working, which can add up quickly over the course of a degree.

Learning on the job

Multiverse’s upskilling programmes provide a unique opportunity to learn artificial intelligence skills on the job. We offer 12 to 18-month programmes in AI, data analytics, and other tech disciplines — none of which require you to leave your current role to participate in.

Our AI-Powered Productivity programme empowers you to use generative AI tools to boost efficiency and output. You’ll learn how to integrate Microsoft 365 Copilot and other cutting-edge platforms in your everyday workflows. The course also covers crucial topics like AI ethics, data privacy, and performance metrics.

The AI for Business Value programme focuses on using artificial intelligence to spark innovation and optimise processes across the business. It combines AI fundamentals with business analysis skills, giving you the tools to drive organisational change. Plus, you’ll learn how to communicate the business impact of AI initiatives to non-technical stakeholders.

This modern approach combines the flexibility of online learning with the opportunity to receive personalised feedback from our dedicated instructors.

Unlike university and bootcamp students, you won’t have to pay a hefty tuition bill or reduce your earning potential. Our upskilling programmes are funded entirely by your employer, and you’ll keep earning a salary while you learn.

Stay competitive with in-demand AI skills

The AI revolution is already in full swing, and the job market is evolving at lightning speed as employers scramble to keep up. In this competitive environment, upskilling early can help you gain a head start and seize exciting — and potentially lucrative — career opportunities in data analytics, AI consulting, and other areas.

As Anna Wang, Multiverse Head of AI, observes, “It’s time to get employees up to speed on AI to even the data skills playing field and give individuals the opportunity to accelerate their careers.”

Multiverse’s upskilling programmes are the only way to gain AI mastery while earning a salary. You’ll study critical AI concepts and start applying your skills in the workplace from day one. Explore our AI for Business Value and AI-Powered Productivity programmes for more information, or fill out our quick application to get started today.

Tech company Multiverse, which has recently introduced powerful AI capabilities across its offerings, will launch the AI-Powered Productivity apprenticeship, the UK’s first accredited apprenticeship to fully embed Microsoft 365 Copilot. This program is eligible for public funding via the apprenticeship levy. The skilling of the wider workforce in AI tools is a crucial step to ensuring the productivity benefits are widely felt across the economy.

Research by Multiverse has found that more than half of workers (51%) have received fewer than 5 hours’ training on AI. 63% of tech leaders say the biggest blocker to further AI investment is their teams’ inability to fully use existing AI technology.

The apprenticeship will see learners develop the skills to boost their output at work by using Microsoft 365 Copilot, while understanding the ethical and data protection implications of using AI. It will be delivered using Multiverse’s measured, applied, guided, and equitable approach, which incorporates personalised, on-the-job learning to maximise business impact. The programme is suitable for a wide range of roles and levels of experience.

Launched in 2023, Microsoft 365 Copilot embeds generative AI into Microsoft’s suite of productivity apps – Word, Excel, PowerPoint, Outlook, Teams, OneNote, OneDrive – to unleash creativity, unlock productivity, and uplevel skills.

Microsoft expanded its skilling program Get On – established in 2020 to empower 1.5 million individuals with tech skills by 2025 – with the added aim of equipping 1 million more people with the AI skills ranging from AI fluency to technical and business transformation.

Microsoft UK CEO, Darren Hardman, said: “To fully capitalise on AI's economic potential and drive growth, we must equip people with the necessary knowledge and tools. By investing in AI skilling, we not only enhance our own capabilities but also drive innovation and productivity across the entire economy.

“The AI-powered Productivity apprenticeship from Multiverse is a great example of a programme that places AI and Microsoft 365 Copilot at the heart of building the skills for the future. We are excited to see the impact of this programme on the future workforce.”

The UK’s first edtech unicorn, Multiverse, is a tech company that identifies, closes and prevents skills gaps, through personalised, on-the-job learning, and is one of the world’s largest apprenticeship providers.

AI-Powered Productivity joins a suite of AI apprenticeships launched by Multiverse, including AI for Business Value and Transformative Leadership, targeted at individuals across every age and every stage of a business.

Multiverse has trained people at 1,500 organisations including the NHS, KPMG, and Capita.

Multiverse CEO, Euan Blair, said: “We know that Gen AI will unlock a surge of productivity in UK businesses, but it requires a combination of the right tools and the right skills to be successful.

“That’s why businesses that want to win in the AI age must make a deliberate effort to upskill and reskill workers with what they need to harness this opportunity. We’re taking market-leading tools like Microsoft Copilot and empowering workers to drive real outcomes using them.

“Not only will it enable businesses to get the best out of AI, but it’ll also set individuals up with the skills to drive their careers for years to come.”

Businesses and organisations can enrol their employees onto the programme, where they will cover modules on AI technologies, prompt engineering, data privacy, and tool utilisation. Participants will learn to measure the impact of AI on their roles, advocate for its use in the workplace, and follow ethical practices.

Employers will be able to fund the programme fully from their Apprenticeship Levy, an additional payroll tax, which is ringfenced for apprenticeship training. The Levy is currently set at 0.5% of an employer’s annual pay bill and applicable to employers with an annual pay bill of over £3 million.

Let’s take a closer look at how professionals use maths for data science and how much you’ll need to know to pursue a career in this exciting field.

How do Data Scientists use maths?

A Data Scientist's primary role is to mine, examine, and make sense of data. Maths plays a role in each of these stages.

Data Scientists use mathematical skills to:

• Understand and use machine learning algorithms
• Analyse datasets from various sources
• Identify patterns in data
• Forecast trends and growth

Data Scientists also use mathematical functions to perform data analysis and apply machine learning techniques like clustering, regression, and classification.

Clustering

Clustering is a way to organise data into clusters or groups that share similarities with each other. It involves some calculus and statistics. A clustering algorithm organises data into these groups to identify trends and reveal insights at the surface level.

For example, a company with a large customer base can use clustering to segment customers based on their demographics or areas of interest. When you are promoting products, you can better personalise your marketing messages based on data points like customer location, behaviour, interests, and more.

Regression

Regression analysis is a way to measure how certain factors impact outcomes or objectives. In other words, it shows how one variable impacts another. It uses a combination of algebra and statistics.

Data Scientists use regression to make data-driven predictions and help businesses make better decisions. For example, they can use regression to forecast future sales or to predict if a company should increase the inventory of a product.

Classification

Data classification is the process of labelling or categorising data to easily store, retrieve, and use it to predict future outcomes. In machine learning, classification uses a set of training data to organise data into classes. For instance, an email spam filter uses classification to detect if an email is spam or not.

Foundations of data analysis

All data professionals need a solid grasp of essential mathematical concepts, but that’s only part of the skill set needed to analyse data effectively. The ability to work with diverse types of information and create data visualisations are also crucial for gaining meaningful insights.

Working with different data types

Data Analysts and Data Scientists handle a wide range of data types, including:

• Categorical data: Qualitative information that can be represented by a name or symbol, such as customer demographics and types of products
• Numerical data: Quantitative information, such as conversion rates and sales revenue

You should know how to use Structured Query Language (SQL) to manage categorical and numerical data. This language allows you to query, organise, and filter information in relational databases.

Data visualisation

Data Scientists often transform datasets into accessible graphic representations. These visualisations can reveal previously unnoticed patterns or anomalies in datasets. They also allow data professionals to communicate their findings with non-technical stakeholders.

Platforms like Microsoft BI and Tableau use machine learning models and mathematics to analyse data. They also have intuitive interfaces that allow you to design interactive dashboards and data visualisations. For example, you could use line graphs to represent economic trends over time.

You should also learn how to use data visualisation libraries in Python. Popular frameworks include Gleam, Matplotlib, and Plotly. They have built-in templates and themes that you can use to create polished visualisations quickly.

What types of maths do Data Scientists need to know?

Luckily, you don’t need to be a mathematician or have a Ph.D. in mathematics to be a Data Scientist. Data Scientists use three main types of maths—linear algebra, calculus, and statistics. Probability is another maths data scientists use, but it is sometimes grouped together with statistics.

Linear algebra

Some consider Linear Algebra the mathematics of data and the foundation of machine learning. Data Scientists manipulate and analyse raw data through matrices, rows, and columns of numbers or data points.

Datasets usually take the form of matrices. Data Scientists store and manipulate data inside them and they use linear algebra during the process. For example, linear algebra is a core component of data preprocessing. It’s the process of organising raw data so that it can be read and understood by machines.

At a minimum, Data Scientists should know matrices and vectors and how to apply basic algebra principles to solve data problems.

Calculus

Data Scientists use calculus to analyse rates of change and relationships within datasets. These maths skills help them understand how a change in one variable — such as changing customer preferences — affects another variable, like sales revenue.

Before you begin your data science journey, you should master the two main branches of calculus: differential and integral.

Differential calculus

Differential calculus studies how quickly quantities change. Data Scientists should learn its foundational concepts, including limits and derivatives. Python libraries like NumPy and SymPy can speed up this learning process by performing complex calculations efficiently.

Data professionals apply differential calculus to optimise machine learning models and functions. For instance, gradient descent calculates the error between the predicted and actual results. This method allows neural networks and other types of algorithms to adjust their parameters iteratively, reducing errors and improving performance.

Integral calculus

Integral calculus analyses the accumulation of quantities over a specific integral. To effectively apply this technique, you must understand definite and indefinite integrals. Familiarity with Python libraries like SciPy can also help you calculate integrals.

Data professionals use this branch of mathematics to solve many problems in data science, such as forecasting the demand for a product and analysing revenue. Machine learning algorithms also use integral calculus to calculate probability and variance.

Probability and statistics

Probability and statistics go hand in hand. Data professionals use these mathematical foundations to analyse information and forecast events.

Statistics is the branch of mathematics that collects and analyses large data sets to extract meaningful insights from them. Data Scientists use statistics to:

• Collect, review, analyse, and form insights from data
• Identify and translate data patterns into actionable business insights
• Answer questions by creating experiments, analysing and interpreting datasets
• Understand machine learning and predictive models

Here are a few examples of statistics principles you’ll need to know to break into the data science field:

• Descriptive statistics - Analyses a dataset to summarise its main characteristics, like mean and mode
• Inferential statistics - Extrapolates from known data to make predictions or generalisations about a larger population
• Linear regression - Predicts the relationship between an dependent variable and two or more independent variables
• Statistical experiments - Know how to create statistical hypotheses, do A/B testing and other experiments, and form conclusions

In contrast, probability is the likelihood that an event will occur. Data professionals use this method to analyse risk, forecast trends, and predict the outcomes of business decisions.

Data Scientists need to know these basics of probability:

• Distributions - Summarizes all the possible values in a dataset and the frequency with which they occur
• Statistical significance - Measures the likelihood that a relationship or result isn’t random
• Bayes' Theorem - A mathematical formula used to calculate the likelihood of an event based on prior knowledge and the probabilities of related events
• Hypothesis testing - Determines whether your assumptions about a particular population or dataset are supported by evidence
• Probability theory - Calculates the likelihood of different outcomes of random events or uncertain situations

Keep in mind that how much maths you need to know may also depend on your role. For example, a junior Data Analyst focuses more on analysing trends. Although they still need to know how to extract data and interpret information, they work less with complex mathematical concepts. Unless they need to work with machine learning algorithms, they’ll use maths for data science less than a senior-level Data Scientist.

This is more of an introduction than an exhaustive list of how much maths is involved in data science. If you are interested in learning data science and the maths that Data Scientists use, Multiverse offers a Data Fellowship and a Data & Insights for Business Decisions program.

Data Scientists roles: salary, job titles, and more

Modern businesses generate and collect enormous amounts of data, such as financial transactions, healthcare records, and social media posts. They need workers with hard data skills to analyse this information effectively and support data-driven decision-making.

In the UK, the surging demand for data professionals has far outpaced the available workforce. A study commissioned by the Department for Digital, Culture, Media and Sport found that UK businesses are seeking to fill 178,000 to 234,000 roles requiring hard data skills. However, 46% of the surveyed companies reported difficulty finding qualified candidates within the last two years.

This talent shortage has led many UK businesses to offer competitive salaries and other perks. According to Indeed, the average salary for Data Scientists in the UK is £51,000. To attract candidates with specialised data skills, employers may also offer hybrid or remote arrangements, generous leave policies, and additional benefits.

Professionals often begin their careers as junior Data Scientists or Analysts, but this field has many opportunities for advancement. Here are three job titles you could pursue as you gain experience:

Senior Data Scientist

A Senior Data Scientist leads long-term projects and supervises Junior Data Scientists. They also communicate findings to stakeholders and guide data-driven decision-making. For instance, a Senior Data Scientist might use machine learning algorithms to detect fraud and help business leaders develop new cybersecurity policies.

Salary:

• Starts at - £61,000
• Average base salary - £73,000
• Top earners make up to - £87,000

Source: Glassdoor

Machine Learning Engineer

A Machine Learning Engineer builds, deploys, and maintains machine learning applications. They use maths and data science to design and train machine learning models.

Salary:

• Starts at - £41,000
• Average base salary - £55,000
• Top earners make up to - £73,000

Source: Glassdoor

Data Architect

A Data Architect designs and maintains data structures, databases, and data pipelines. They’re responsible for integrating data from different sources so data flows smoothly throughout their organisation.

Salary:

• Starts at - £55,000
• Average base salary - £68,000
• Top earners make up to - £85,000

Source: Glassdoor

Boost your skills with comprehensive Data Scientist training

A strong understanding of maths is essential for machine learning and data science roles. It can help you solve problems, optimise model performance, and interpret complex data that answer business questions.

You don’t need to know how to solve every algebraic equation — Data Scientists use computers for that. However, you should become familiar with the principles of linear algebra, calculus, statistics, and probability. You don’t need to be an expert mathematician, but you should broadly enjoy maths and analysing numbers to pursue a data science career.

Multiverse’s Data Fellowship and Data & Insights for Business Decisions programs can help you learn the basic maths concepts you need to know. However, the focus is on how to apply those maths skills in data science.

The Data Fellowship guides you through the fundamental principles of data analysis, including identifying and solving real world problems with data. Our modules cover advanced analytics and statistical methods, data visualisation, data management, and other critical topics. You’ll sharpen your skills by participating in data analysis and statistics hackathons.

The Data & Insights for Business Decisions program teaches you how to transform raw data into meaningful insights. You’ll learn how to use popular data analytics tools — including Excel and PowerBI — to clean and manipulate data. The program also teaches you how to tell compelling stories with data and foster a data-driven culture in your organisation.

Upskillers don’t pay for tuition — programs are free. You actually get paid to work in a data role and learn while you complete the program. You’ll also start immediately applying your new skills by working on real projects for your employer, accelerating the learning process.

The first step is to apply here. If accepted, you’ll start learning data science and get on-the-job training at a company that pays you for your time.