Author: Marisa Crous

How Cheta Landed a VP Product Manager Role At JPMorganChase

Posted on by Marisa Crous

Cheta Anietie-Effiong is the VP Product Manager at JPMorganChase, based in London. In an Ask Me Anything (AMA) webinar, Cheta shares how the King’s Product Management Career Accelerator helped her structure years of hands-on product experience and land a senior role while still on the programme.

Learning product the hard way

Cheta began her product career at a startup. It gave her real experience fast, but very little structured learning. As she puts it, at a startup you’re often “scrambling to figure stuff out”, and that can leave you unsure whether you’re doing the right thing.

She knew the work, but she wanted the frameworks behind it and the confidence to make the jump to a more senior role. The King’s Product Management Career Accelerator had sat “on my vision board” for a couple of years before she finally enrolled.

Is it right for someone already in product?

Cheta had three reservations before she signed up. The first was cost. Talking through the payment options with an advisor helped, and she chose to see the fee as “an investment in myself, and an investment in my skills and in my career”.

The second was time. She’s honest that the programme is demanding: “It’s very, very time-consuming in the best way possible.”

The third was whether the Career Accelerator was only for people without PM experience. Reading about a past learner in a similar position to hers settled that question.

Real PM work in every assignment

The project-based approach was one of the reasons Cheta chose this programme. Every assignment mirrors the job: “you do get given assignments, and those assignments are actual tasks that you would do as a product manager in real life.”

Her Course Facilitators, product managers themselves, showed how each framework is used in day-to-day product work. That depth held up for someone already in the role: she found it “so, so useful, even as, you know, a PM myself already.” She also brought real problems from her own job to the facilitators, asking, “If you were in my shoes, what would you do?” and found them very responsive.

The programme also helped her with product language, something she’d found hard to pick up on the job: “Learn how to speak like a PM, act like a PM.” One of her favourite parts was building her own low- and high-fidelity prototypes.

Making it work around family and a full-time job

Cheta studied as a working mum with a young baby, and she doesn’t pretend it was easy. There were late nights. She worked through course material in the evenings after her baby had gone to bed and on parts of the weekend.

What helped most was going to as many live sessions as she could, which ran at lunchtime or in the evening. Attending “as many of the live sessions as and when they were happening” meant she never built up a big backlog. Recordings were there when she missed one, and she often rewatched sessions she’d already attended.

A Success Manager, a coach, and a 3am WhatsApp group

Support is built into every Career Accelerator. Early on, Cheta’s Success Manager sent weekly lists of what to complete, which she found really helpful. Her 1:1 sessions with her Career Coach gave her space to talk through her thinking and decisions, including the emotional side of returning to work after maternity leave, and to get clear on what she wanted from her next role.

Then there’s the cohort, a mix of career starters, career changers, and experienced PMs from different industries. Learners set up their own WhatsApp group, where “you can find people at 3am asking questions, and somebody will respond.”

Confidence, one presentation at a time

Each assignment ends in a presentation, pitched as if to stakeholders and kept to a set time. In her first two, Cheta felt flustered. By her most recent one, something had shifted: “oh wow, okay, I’m learning, I’m getting better at this.”

For her, confidence and presentation skills are the most valuable things she’s gained. As she explains, “as a PM, you definitely need to be confident”, because so much of the job is talking to senior stakeholders.

The interview that felt different

Cheta was already applying for roles when she started the programme, aiming to land a senior position by the end. Instead, she secured her VP role within the first two months.

She believes the programme teaches you to think like a PM, which she sees as one of the main things interviewers look for: how you analyse scenarios, present data and make decisions. What stayed with her was how the interview felt. 

Partway through, she caught herself thinking, “you actually know this stuff”, and she walked out thinking, “I left that interview feeling like, yeah, I think I got it.” Whatever the result, she says, “I felt like I did a good job, and I did myself justice, and I think that’s so important.”

Name the gaps, then close them

After almost a year away from work, Cheta says, “I had a lot of imposter syndrome.” Her approach was to name the gaps and close them: “Okay, I accept that I don’t know XYZ, how can I then know XYZ?”

She also warns against comparing yourself with people who’ve been in product for a decade: “They were also beginners at some point, right?”

For career changers, her advice is to use the domain knowledge you already have, translate your existing skills into product terms, and volunteer for digital projects where you work now. As she reminds the audience, “You might not have this PM title, but you have the PM experience.”

And to anyone still on the fence, her message is the one she’d give her pre-programme self: “take the plunge.”

Have the experience? Add the structure.

Formalise what you already do and build the confidence to step up with the King’s Product Management Career Accelerator.

Six months, part-time and fully online, with 12 months of 1:1 career coaching and a certificate from King’s College London.

Download the brochure to see the full curriculum and how the support works.

This King’s College London programme is designed and delivered in partnership with FourthRev.

How Beatrice Frascatani Built Her Digital Marketing Foundation With LSE

Posted on by Marisa Crous

Plenty of people work alongside marketing for years without ever being taught it. Beatrice Frascatani, Co-head of Communications at Alda, was one of them. In a recent Ask Me Anything session, she shared what it took to fill that gap while working full-time, and what she does differently now.

Hitting a ceiling as deputy head

Beatrice’s path into communications started with politics and journalism. She studied political science and international relations in Italy, then completed master’s degrees in Amsterdam and Brussels, the second in media and journalism. 

A traineeship in public policy communication at a Brussels consultancy introduced her to the field, and she later joined a non-profit back home as a communications manager and communications officer.

Over six years, she grew with the organisation. When her supervisor went on maternity leave, Beatrice stepped up as acting head of the communications department, leading a team of eight. When her supervisor returned, Beatrice became deputy head and started asking herself a familiar question:

“How can I improve my career? How can I grow a little bit more professionally?”

She felt she’d reached the most she could in that role. Then marketing found her. Her non-profit runs a for-profit organisation that wasn’t performing well, and Beatrice offered to take on its communications. 

Despite five years in the field, it was harder than she expected. Non-profit communication, she explained, is about showing impact, not selling. In marketing terms, she was missing the basics. Coming from a communications background, she was less familiar with certain marketing-specific concepts, such as lead generation.

That’s why picking it up on the job wasn’t an option. She needed the theory first.

Six months over two weeks

Beatrice researched her options carefully. The programme had to be in English. She wanted structured content, the university and companies behind it, and learning she could do around her job. And she weighed quality against cost.

Courses lasting one or two weeks made her question what she could realistically learn in that time. A six-month programme with a Career Coach was different. She’s honest that the fee was a big decision, calling it “an investment for your life” and taking time before committing. Her verdict now: “I don’t regret it.”

Studying around a job that travels

Her biggest hesitation was time. Her job is demanding, she lives away from her home city and travels a lot for work. English isn’t her first language either, and it had been a while since she last earned a degree.

So where did the hours come from? “From weekends, that’s the real answer,” she said. She also studied after work, used some of her holiday, and told her organisation what she was doing. They were supportive, seeing the value it would bring them too. She didn’t sugar-coat the commitment:

“To be honest, it’s not really 16 hours a week, it’s a little bit more.”

A third read in a Brussels hotel

Her hardest moment came in Course 2, which covers data. Studying in a Brussels hotel during a work trip, she found herself reading the same module for the third time without it sinking in.

“Is it really worth it? Like, I’m really struggling. Am I capable of doing that?”

What changed things was talking to her peers. Everyone was finding parts of it hard, and the cohort’s WhatsApp group became an open channel for questions and doubts. Her takeaway: “you’re not alone in this process.”

Her success manager helped too. Beatrice mentioned they can arrange extensions, discuss pausing the programme if life gets in the way, and run weekly time-management sessions. “I really felt supported.”

From personas to team one-to-ones

She particularly enjoyed discovering new concepts, such as marketing attribution, and learning how to apply them in practice. For instance, attribution gave her valuable insights into which channels and activities generate results, helping her focus her efforts more effectively. The data module, though tough, taught her to handle data ethically and made GDPR far clearer.

The biggest shift came in Course 3, on digital marketing leadership. Beatrice realised she’d led a team of eight without ever learning how to lead. Her conversations had always focused on the organisation’s objectives, never her team’s own goals. After the course, she started one-to-one calls to understand what each person wanted, which gave her useful insight for the work itself, and for herself:

“So I understood what I was doing wrong, what I was doing right, where I could improve.”

She also came away more confident with AI. She’d used it a little in her communications work before, and the programme asked her to assess emerging tools and decide where they could improve an existing process. Her view is that AI keeps changing, so keeping up is ongoing, not a one-off.

A strategy across three time zones

The final six weeks were the Employer Project, a live brief from a real company that wanted to reach an audience it was missing. Beatrice’s team worked with the company’s real data, website and social media.

It was challenging. Teammates were spread across time zones, including the US and Singapore, and when one member hit a personal problem, the group regrouped and kept the deadlines. Beatrice says it taught her a lot about handling the unpredictable. Presenting taught her something else:

“You need to be confident about all the work that you did.”

The employer was very happy with the strategy, and some of the team’s suggestions were ideas the company hadn’t considered.

Her teammates left a mark as well. Some were working full-time while raising children, and seeing what they managed gave her “even more motivation than the one I had at the very beginning.”

Slowing down to build a strategy

Beatrice finished the programme in June and now manages communications and marketing for the for-profit organisation. Her first realisation was that strategy needs time. 

Both organisations had grown so rapidly that their strategies had not yet fully evolved to keep pace with their growth, so she asked the executive directors to sit down and build one, starting with who their audience is. She now spends time on research and competitor analysis before anything else, because she sees strategy as “the base for everything”.

Her Course 3 project, applied to her own organisation, produced a new fundraising option that’s now part of her work. The new role also brought more responsibility and a salary increase. 

Her Career Coach helped her manage anxiety around public speaking and imposter syndrome, and she brought her challenges she had to each session.

Her advice to prospective learners

She already had experience creating social media content and newsletters, but wanted to deepen her understanding of more data-driven approaches, such as experimentation and A/B testing. She didn’t have any marketing background, and in the end, she managed to do it.

She said people without a digital marketing background “should not worry about that, because you will have it afterwards.” 

If you’re weighing a similar move, download the brochure for the LSE Digital Marketing Strategy & Analytics Career Accelerator to see how six months of online, part-time study, a live Employer Project and up to 12 months of 1:1 career coaching fit together.

How Fernanda Aventurato Used Her Second Career Accelerator to Build Her Digital Marketing Skills

Posted on by Marisa Crous

Fernanda Aventurato, Creative Project Manager at Birlasoft, built the early part of her career in broadcast and TV production before moving into a corporate B2B tech role, where, at the time of the AMA, she worked across partnerships and marketing. 

In an AMA webinar, Fernanda shares why she returned for a second Career Accelerator and what it took to study the LSE Digital Marketing Strategy & Analytics Career Accelerator alongside a full-time job.

She had studied digital marketing before, back in 2016. But as she explained, that no longer felt like enough: “the market is completely different.”

“I really felt this gap”

Fernanda already knew a little about go-to-market strategy, and she wanted to go deeper. Her first step was the King’s Product Management Career Accelerator, delivered with King’s College London. She calls it “amazing”, and its final course was on go-to-market strategy. As she puts it, “it stayed on me.”

A year later, she decided to do a second Career Accelerator. This time, she knew what she was signing up for: “I knew already what was happening with me, the learning curve and everything, so that’s why I decided to apply again.”

Her goal was clear. She wanted the most current skills, and she says she “needed to feel confident in order to show up to the market and say, look, I’m capable to do that.”

When she read through the programme details, delivered with the London School of Economics and Political Science (LSE), it covered what she was looking for: strategy, how to apply it, some leadership, and an Employer Project.

Applying it at work, one assignment at a time

For Fernanda, the programme felt practical because every assignment was built around a real-world challenge. She didn’t wait until the end of the programme to use what she was learning.

“I had an assignment, and I felt so confident after that that I did the same for my company. I said, ‘Oh, you know that I can do that.’ And that was the first thing that they told me: ‘You are doing really well here,’ because before we were hiring a really expensive third company to do that… and I’m able to get there.”

The second course raised the stakes. Her assignment asked her to plan a campaign with a much bigger budget and prove she could deliver results. That meant learning to analyse data properly, and it gave her the confidence to go to her company and say, “Look, we can deliver ROI because of this, this, and that.”

Before the programme, she says, vanity metrics made it hard to know what really mattered. Learning to read and interpret data changed that. Her summary is simple: “Without data, you are blind.”

“Things are evolving so fast with AI,” Fernanda says, and she saw that change up close in the Masterclasses with practitioners, moving from looking beyond SEO to GEO (generative engine optimisation) to using AI for her own benefit. In her words, “it opened your eyes to what is happening at the moment.” In the data analytics course, she also used AI to run a cluster analysis of her target audience.

A different way of seeing the work

The third course, on leadership and innovation, shifted how Fernanda thinks about her role. For her, leadership isn’t only about managing people. It’s about stepping outside your own tasks and seeing how a workflow could improve.

She took the leadership project she completed on the programme and built it for her own company. At the time of the AMA, she did it with her manager, and she hoped it would go further. The change was visible day to day, too:

“Even inside of my company today it was very clear how I grew in the meetings, challenging some results that I think could be better, so I’m much more capable to add value [to] my company for sure.”

A live brief, and a reason to stay involved

The programme ends with a six-week Employer Project, a live brief from a real organisation. Fernanda’s team worked with Cancer Support Netherlands, a volunteer-led charity, to build its digital marketing strategy. 

“The employer project for me was very challenging, but I loved every second, and the strategy that we delivered to them was very good. I’m very proud of what my team delivered, and after that I was so interested to see that happen… that now I’m volunteering for them.”

When she spoke at the AMA, two peers from her cohort had joined her as volunteers, one based in the Netherlands and one “completely on the other side of the world”.

Support for the days that aren’t good ones

Fernanda describes herself as shy. Her Career Coach was part of the reason she agreed to speak at the AMA at all.

“The fact that I’m being here today speaking with you, because I’m very shy, it’s a work that I had with one of the career coaches that said to me, ‘you need to challenge yourself’.”

She worked with a different Career Coach on each of her Career Accelerators: “They were very different, and they helped me in different ways.”

Coaching also helped her position herself by helping her understand her mix of skills and how to present them.

She found the same steadiness in her Success Manager because, as she put it, “it’s not every day that’s a good day, to be very honest.” On the harder days, the success team helped her stay on track.

Studying after hours, with a few days off at deadlines

The programme asks for around 16–20 hours a week, and Fernanda is clear that it takes planning. Her biggest challenge was time, along with overthinking her first assignment because she wanted to do her best. Looking back, her advice is to lean on the Course Facilitator and success team rather than carry the stress alone.

She studied after work and some weekends, and arranged a few days off around assignment deadlines with her manager, something she hadn’t done on her first Career Accelerator. Her view: “It’s doable, you can do it, but you need to know that there is dedication.”

She’s just as direct about the LSE name: “If you’re looking for something easier, don’t look for LSE. But there is a guarantee that you’re going to learn.”

Taking charge of her next move

At the time of the AMA, Fernanda’s goal was to move fully into marketing, and she wasn’t waiting for it to happen to her:

“I just finished… and I’m getting answers for the applications I’m doing, and I’m feeling confident in the interview, so this is amazing. This is already a big step because before, I didn’t even have replies, and now I know exactly what my strengths are.”

One lesson from coaching stuck with her: “You are responsible for your career.”

Asked whether it was worth the investment, she pointed to the fact that she has made it twice: “considering that this is the second time that I do this investment, it’s obviously that it’s worth, right?”

Her advice to anyone considering the programme is just as direct. Use everything it offers, from the Masterclasses to your coach and the success team, and “enjoy the journey and get the most of that.” If you want to bring your marketing skills up to date, backed by strategy and data analysis, explore the LSE Digital Marketing Strategy & Analytics Career Accelerator: six months online and part-time, with up to 12 months of 1:1 career coaching.

How Faye Grant Went From Fashion Merchandising to Data Analytics

Posted on by Marisa Crous

Faye Grant is the Franchise Development and Data Manager at Puddle Ducks, a role she started the month after she finished the LSE Data Analytics Career Accelerator. In an AMA webinar, she shares how she went from forecasting stock in spreadsheets to building her own dashboards, and why she now enjoys her work more than in any role before.

Investigating why, not just reporting the numbers

Faye’s interest in data started in her day job. She studied fashion business and management at university, then worked in merchandising and demand planning roles at several fashion companies. She used sales and stock data to forecast demand and decide how much of each product to buy, mostly in Excel and Power BI.

That was the part of the job she enjoyed most. She “really enjoyed investigating why something was happening, rather than just reporting the numbers”, and wanted to work with data more deeply than her role allowed.

The skills gap she found in data job ads

Faye assumed her transferable skills would be enough to move into a data role, until her research showed “quite a big skills gap”. She looked at data roles on LinkedIn that appealed to her and flagged every unfamiliar term in the job descriptions. The gaps were SQL, Python, R, and the broader analytical techniques behind them.

That was when she decided to make what she calls a “deliberate career transition”. She didn’t want to “become too niche too soon”, so she looked for a programme that would prepare her for roles leaning towards coding as well as visualisation, and chose the LSE Data Analytics Career Accelerator.

Learning to see errors as part of the process

Faye came to see her mistakes as part of the learning. Moving from Excel into programming languages was hard at first, and she’s honest that self-doubt came with it. She used to struggle when things didn’t work straight away. Over time, she saw that every fix taught her how to solve the same problem next time, and that “when things are going wrong, it can be a bit of a blessing”.

She didn’t work through it alone. Live calls and posts on the learning platform showed her that other learners faced the same problems, and people with different strengths helped each other. Her Success Manager was always someone to “bounce some ideas off”. Support is built into every Career Accelerator, and for Faye that meant peers and a Success Manager to work through problems with. Her takeaway: “finding something difficult doesn’t mean you’re incapable of learning it.”

As a practical learner, she found the weekly tasks the most useful part of the curriculum, because “repetition is such a good way of learning those skills”. That is the idea behind project-based learning.

A daily target, around 15 to 20 hours a week

Faye kept to a daily target. She studied while travelling, so she set a fixed number of hours and a goal for each day, and stuck closely to the suggested time for each module. Most of the learners she spoke to worked full-time and studied in the evenings and at weekends. She puts the average at around 15 to 20 hours a week, with more before a submission and a lighter week after it.

A team of six and data the business planned to use

The programme finishes with a live brief from a real company, and Faye’s team delivered work the company asked to use. For the Employer Project, she worked in a team of six. They divided the work by strength (coding for some, visualisation for others) and presented their findings to the employer at the end.

She says, “…it did really feel like we were doing a project for the employer, which I guess we were”. Working together online also prepared her for her current job, much of which happens online too.

Reframing a merchandising CV for data roles

Faye found her 1:1 sessions with her Career Coach a big help in the move into data.

“She helped me with what sites to look on for data jobs, key buzzwords that you perhaps wouldn’t think of, and also reframing the past experience that I do have, and how transferable that is.”

Her coach also helped her tailor her CV to specific roles and prepare for interviews. With so many polished CVs in circulation, Faye found that a portfolio of projects, including her Employer Project, gave employers something more concrete to look at.

Asking better questions, a few months into the job

One of the most useful skills Faye brings to work is knowing what to ask before starting a piece of work. A few months into her role, she uses SQL, Python, Tableau and Excel. On the programme, each brief set out who the work was for and what they expected. At work, that isn’t always the case, so “it’s knowing those questions to ask to get all the information that you need.”

She’s also building her own dashboards in Power BI, which came easily after Tableau.

The cheat sheet she’d start on day one

Faye would do the programme again, and this time she’d start a cheat sheet on day one, noting quick fixes and standard steps for each tool as she went. She feels the investment has been worth it, pointing to her salary, the LSE connection and the portfolio she can show employers.

She’s been researching AI on her own and plans to keep learning, because “things do move really quickly”. Longer term, she’d like to take on short-term data projects for smaller businesses. For anyone unsure about committing, she points out that “6 months is such a short amount of time to dedicate towards yourself and potential career growth”.

If you want to move from reporting numbers to explaining them, explore the LSE Data Analytics Career Accelerator. It runs online, part-time, for six months, with 12 months of 1:1 career coaching and an LSE certificate.

This LSE programme is designed and delivered in collaboration with FourthRev.

The Human Skills That Get You Hired in Tech

Posted on by Marisa Crous

When you move into tech, your technical skills get you onto the shortlist. What gets you the offer is usually something else. Ask a hiring manager why one candidate won out and they tend to talk about how the person explained their thinking, how they worked with the rest of the team, and how they handled a problem they had never seen before. Those are human skills, and they settle a lot of hiring decisions.

There is a good reason to take them seriously right now. As AI takes on more of the routine technical work, human skills are becoming the thing that sets candidates apart. AI can draft code or summarise a dataset in seconds, but it cannot read a room, weigh a trade-off, or decide what is worth building in the first place. The World Economic Forum’s Future of Jobs Report 2025 makes a similar point: alongside technical ability, employers rank analytical thinking, resilience, flexibility and leadership among the skills they will need most by 2030.

If you have spent years in any professional role, this works in your favour. You are not starting from scratch. You already have a lot of what employers value most, and the job is to add a current technical layer on top of it. Below are the ten human skills that come up most often in tech job specs, the roles that ask for each, and how to turn them into a genuine career move.

Why human skills matter more as AI spreads

For a long time, the standard advice was simply to learn to code. It’sstill worth doing, but It’sno longer the scarce part. As AI tools lift the baseline on technical tasks, teams need people who can do the work around the technology: making sense of ambiguity, working across different functions, and thinking through the effects of what they ship. The WEF’s skills outlook estimates that a large share of workers’ core skills will shift by 2030, and it points to upskilling as the priority. That’s exactly the gap a focused programme is built to close.

For experienced professionals, that shift is encouraging. Judgement, clear communication and the ability to stay level-headed when a project goes wrong take years to build, and there is no shortcut to them. The technical toolkit, by comparison, is the faster part to pick up.

The ten human skills tech employers rank highest

1. Communication

Explaining your thinking clearly, to technical and non-technical people alike, matters in almost every tech role. So does writing precisely, whether you are documenting a project or briefing a stakeholder. Common in roles like Data Analyst, Data Scientist, Product Manager, UX Researcher and Technical Lead.

2. Innovative thinking

Employers want curious people who question assumptions, ask good questions, and run small experiments to improve how things work. Common in roles like Software Engineer, Solutions Architect and AI Engineer.

3. Teamwork and collaboration

Modern tech work has many moving parts and many people. Being a dependable teammate who listens and engages with other ideas is a real asset. Common in roles like Product Manager, Engineering Manager, UX/UI Designer and QA Engineer.

4. Leadership

You do not need a senior title to be asked for leadership. Employers look for people who take accountability, welcome feedback, handle disagreement well, and stay decisive under pressure. Common in roles like Product Manager, Project Manager, Data Scientist and AI Engineer.

5. Problem-solving

As business and technology overlap, the strongest candidates can size up a problem quickly and take a practical approach to solving it. Common in roles like Software Engineer, UX Designer, Cyber Security Analyst and Cloud Architect.

6. Critical thinking

From questioning a hypothesis to checking an analysis before trusting its results, the ability to think logically and work systematically underpins most technical roles. Common in roles like Data Analyst, Data Scientist, AI Engineer and Systems Analyst.

7. Emotional intelligence

In fast-moving teams, self-awareness keeps relationships and projects healthy. Staying calm under pressure, listening well and taking feedback gracefully all count. Common in roles like UX Researcher, Product Manager, Technical Project Manager and Solutions Architect.

8. Organisation

Strong organisational habits let you structure information, prioritise, and hit deadlines across competing tasks. It’sas much a mindset as a set of tools. Common in roles like Product Manager, Data Analyst, Technical Lead and Application Developer.

9. Detail-orientation

A lot of technical work needs precision. Being observant and thorough, and catching mistakes before they cause damage, is highly valued. Common in roles like Data Analyst, UX Designer, Front-end Developer and Full-stack Developer.

10. Flexibility and adaptability

Setbacks and changes of direction are normal in tech. Employers value people who adapt without frustration and keep their focus on the solution. Common in roles like UX Researcher, Cyber Security Analyst, Software Test Engineer and Back-end Developer.

Career-changers already have the difficult part

Read that list again and notice how much of it you already do. If you have managed projects, handled clients, led a team, or worked through a reorganisation, you have been practising communication, leadership, problem-solving and adaptability for years, often at a level that junior candidates have not reached yet.

That’s the point worth holding on to. The human skills take the longest to develop, and you have already put in the time. What is missing is the current technical vocabulary of the field you want to move into, and that’s the part designed to be learned. If you are not sure how to present your existing experience for a new industry, our guide to transferable skills for career-changers is a good next read, and careers you can pursue with data analytics skills shows where one set of skills can take you.

How to add the technical layer

The quickest way to pair your human skills with employer-recognised technical training is a structured programme built for working professionals, rather than a stack of self-paced courses you will never finish. If the idea of a career accelerator is new to you, start with what a career accelerator is and who it’sfor.

A FourthRev Career Accelerator is built for this. You study part-time around your job, work on real projects set by real employers, and earn a certificate from a world-leading university, with 1:1 career coaching throughout. You get the technical training and the credibility signal without stepping out of the workforce.

Where you point those skills depends on the field you are drawn to:

  1. Analytical, detail-oriented and curious about data: the LSE Data Analytics Career Accelerator or the Cambridge PACE Data Science with Machine Learning & AI Career Accelerator.
  2. A strong communicator who likes solving user problems: the King’s UX & UI Product Design Career Accelerator.
  3. A natural organiser who connects business and technology: the King’s Product Management Career Accelerator.
  4. Creative and analytical about audiences and growth: the LSE Digital Marketing Strategy & Analytics Career Accelerator.
  5.  A leader who wants to guide AI adoption rather than write the code: the LSE AI Leadership Career Accelerator.

If you are still weighing your options, Which Career Accelerator Should I Choose? walks you through it.

People who have already made the move

It helps to see how this plays out in practice. Take the mid-career shift from engineering to data analytics, or the many other paths in our learner success stories, where people brought years of experience, added a technical qualification, and moved into a new field. Their human skills were not a footnote in those stories. They were often the reason it worked.

Where to start

Technology keeps changing, and the human skills that make people effective around it do not. In an AI-shaped workplace, those skills are becoming more valuable, not less, and if you have built a career in any field, you are further along than you think. Add the technical layer, and you are not a beginner. You are an experienced professional with a new toolkit.

Ready to make your next move? Explore the programmes or book a call with an enrolment advisor to talk through the right fit.

FAQ

Do you need soft skills to work in tech? 

Yes. Technical skills get you shortlisted, but employers consistently name human skills such as communication, problem-solving and collaboration as the deciding factor in who they hire and promote. As AI automates more routine technical work, these skills are becoming even more important.

Which soft skills matter most for tech jobs? 

Communication, problem-solving, critical thinking, collaboration and adaptability appear most often in tech job specs, alongside emotional intelligence and leadership, even in non-management roles.

Can career-changers compete with younger, more technical candidates? 

Often, yes. Experienced professionals bring communication, judgement and leadership that take years to develop. Paired with an up-to-date technical credential such as a university-backed Career Accelerator, that experience becomes a genuine advantage rather than a gap.

Will AI make soft skills more or less important? 

More important. AI raises the baseline on technical tasks, which moves the differentiator to the human layer: deciding what to build, working across teams, and handling ambiguity, which are things AI cannot do for you.

How do I show soft skills on a tech CV or in interviews? 

Use evidence rather than adjectives. Instead of listing “great communicator”, describe a time you explained a complex idea to a non-technical stakeholder, or resolved a team disagreement to keep a project on track. Real projects, like those built into a Career Accelerator, give you exactly these stories to tell.

How Paul Soares built a stronger framework for AI with the AI Leadership Accelerator

Posted on by Marisa Crous

Paul Soares is Commercial Director and Head of Operations at Westminster Asset Management in Jersey, where he helps lead a regulated private client and asset management business. 

In an AMA webinar, Paul shares his experience on the LSE AI Leadership Accelerator and how he was already applying what he’d learned at work while still completing the programme, which he has since successfully completed.

“I now move more quickly to underlying questions: what problem are we actually trying to solve? What decision are we trying to improve? Where is the real value? What has to be true for this to work? What governance needs to sit around this? That sounds simple, but it’s quite a significant shift from where I was before.”

For Paul, that shift is the heart of what the LSE AI Leadership Accelerator has given him. 

His career has been built around growth, transformation, operating models and governance in a trust-based, regulated industry. He has also trained in Lean Six Sigma to Green Belt level, which shaped how he thinks about process discipline, control, and change that scales.

When AI became a leadership question

Paul’s decision to study came from a change he saw in his own role.

“AI had stopped being an interesting external topic and had become more of a real internal leadership question.”

Part of his job is to help lead the business through growth, operating discipline, governance and change, so AI couldn’t sit on the sidelines.

“So that means I can’t look at AI as something separate from our business. I have to think about it in terms of value, implementation, control, accountability, and readiness.”

He also saw a gap between how vendors present AI and what it takes on the buying side to make it useful, governable, and commercially credible. He noticed plenty of discussion about AI but less practical understanding of how to lead it inside a regulated business. He wanted a structured approach and a framework to work from.

Choosing a programme built for leaders

Paul compared several options, some more technical, some focused on finance and some only focused on regulation or specific tools. He found the LSE Accelerator through his own research, using AI to help.

“I researched a number of AI and leadership programmes, including options from leading universities in the UK and the US. The LSE AI Leadership Accelerator stood out because the skills and methods could be deployed immediately in my working environment.”

The deciding factor was fit, because Paul’s goal was to lead AI implementation more effectively rather than to become more technical for its own sake. 

“This course is clearly designed for leaders, rather than for specialists. It focuses on strategy, decision making, governance, implementation, and really, at the core of it, business case thinking.”

Learning that went straight to work

Paul says he didn’t expect the depth the programme would reach, particularly on governance, leadership, and business cases. He also didn’t expect how soon it would show up in his day-to-day work.

“I found myself using the thinking almost straight away in discussions around operating models, management information, workflow designs I’m implementing, and governance and decision support. That is what makes it practical – it improves the quality of the discussions and conversations you’re already having.”

His organisation is now carrying out a full review to get the business ready for AI, covering infrastructure as well as software. That work includes putting guardrails around the firm’s chosen AI tool, reducing the risk of data leaking through staff using their own AI tools, and building a policy, framework, and phased implementation plan. 

Paul says the programme has benefited both him and his organisation while he has been studying. The programme also changed how he approaches business cases.

“One of the risks with AI is that organisations can move too quickly from the possibility to the assumption.”

“Business cases are rarely just about what the technology can do. They’re about the value, the timing, the readiness, the confidence, and the control.”

Governance inside the design

For a regulated investment business, governance is critical, and the LSE Accelerator reinforced its role.

“What the programme has reinforced for me is that governance can’t sit outside AI adoption – it has to be inside the design from the get-go. In a regulated environment, the key question is not simply whether something is possible. It’s whether it’s proportionate, governable, and explainable to the regulators.”

Paul’s operational background already led him to think about how change gets embedded. The programme added a more explicit leadership dimension to that thinking, which he says he had not fully used before.

Coaching that turns ideas into action

Paul singles out the 1:1 leadership coaching that runs alongside the courses.

“At a senior level, the question is often less about whether the content is good, and more about how you apply it, how you position it, how you use it to sharpen your leadership focus.”

After one coaching session, he was set a task to complete before the next. 

“In the month that intervened, that task and the outcome from it delivered huge value – not only to me personally, but hugely to my organisation as well.”

He is clear about where the responsibility sits.

“Your coach is there to steer you, but you’re the engine that needs to drive that thinking.”

Confidence, credibility, and a wider view

Paul describes his AI confidence as having grown, though not in a technical sense.

“My confidence has most definitely shifted – but I would describe it as executive confidence, rather than technical confidence. I feel more confident because I now have a stronger framework and clearer language for discussing AI with senior stakeholders.”

He also sees the LSE name as adding weight in conversations with executives, boards, clients, and wider stakeholders.

“It matters because it signals that this is not just a passing interest. It reflects a serious investment in building leadership capability around AI at a time when many organisations need exactly that.”

His cohort added another dimension. Coming from investment management, Paul valued hearing from peers in a wide range of industries, from large corporates to self-employed professionals. Their use cases differed, but he found many of the leadership questions were the same: how to create value, manage risk, lead change and build credibility.

Fitting the programme around a senior role 

Paul is honest that the flexible format still takes effort.

“Applying and undertaking this course still requires a huge amount of discipline.”

What has worked for him is treating the programme as part of his current leadership challenges rather than something separate from work, so the time feels relevant from week to week. He cannot attend every live session, but recordings let him catch up in his own time.

Sharpening, not reinventing

Looking ahead, Paul believes future leadership roles will increasingly require the ability to connect commercial value, governance, change, and technology.

“I wouldn’t describe it as changing the fundamentals of who I am professionally – I describe it as strengthening and sharpening them.”

His advice to senior professionals under pressure to lead AI is to first clarify the capabilities they need. Deep technical training and narrow regulatory updates have their own routes. 

If the need is to make good leadership decisions about where AI adds value, how it should be governed, how to build a credible case and how to lead adoption responsibly, he sees the LSE Accelerator as a strong place to start.

And the thing he wishes he had known before he began?

“I didn’t know how quickly it would improve the quality of my questions. I expected to gain knowledge, but what I found was a stronger decision framework.”

Lead AI in your organisation with the LSE AI Leadership Accelerator. Over six months online, you’ll build four board-ready deliverables (a board advisory memo, a business case, a change management strategy and an executive AI playbook), supported by 12 months of 1:1 leadership coaching.

Download the brochure here.

The Data Science Hiring Code: What Elite Employers Actually Want In 2026

Posted on by Marisa Crous

The data science talent shortage continues to shape the future of work.

According to the World Economic Forum’s Future of Jobs Report 2025, AI and big data represent the fastest-growing capability globally, with 11 million new roles expected by 2030. Yet 63% of employers say skills shortages remain the biggest barrier to business transformation. In the UK alone, more than 178,000 specialist data roles remain unfilled.

For professionals looking to become data scientists, this presents a significant opportunity. But success depends on more than technical expertise. Employers now want professionals who can connect data science to business value, build production-ready solutions and make confident, evidence-based decisions. 

Those themes were at the heart of the recent Cambridge PACE x FourthRev webinar, The Data Science Hiring Code: What Elite Employers Actually Want in 2026. Guest speakers Kate McDermott, Director at Omnis Partners, and Robert Hardman of Delaware Consulting drew on decades of experience in AI recruitment and enterprise transformation to share a clear picture of what employers are looking for in data science talent today. 

Business problem-solvers, not homework problem-solvers 

Robert Hardman opened with a perspective that framed the entire discussion.

“We’re not here to solve homework problems, we’re here to solve business problems.”

Today’s employers want to know:

  • Can you identify the right business problem?
  • Can you explain why your model matters?
  • Can you communicate recommendations that influence decisions?
  • Can you defend your conclusions with evidence?

As Hardman observed:

“Processes are a straitjacket to innovation.”

These are the skills that set top data scientists apart today. 

Hardman also challenged the way many organisations think about AI.

“AI is not about automating the past, it’s about reimagining the future.”

For organisations adopting AI, success comes from challenging old ways of working, spotting new opportunities and using data to make better decisions.

Kate McDermott sees the same pattern from the hiring side.

Technical competence gets you noticed, but commercial awareness and the ability to deliver measurable business impact set top candidates apart. 

As she explained:

“If I’m looking at a CV and it’s littered with commercial results and improvements and an understanding of the context of what you’re doing, that is what makes me stop in my tracks and pick up the phone and call that person.”

For candidates, that means showing examples of how their work helped a business to grow. That emphasis on business application is increasingly reflected in how leading programmes are designed, with learners expected to solve real organisational challenges rather than purely academic exercises. 

Productionisation has become the new hiring benchmark

Today, employers want people who can take an idea beyond a proof of concept and turn it into something the business actually uses. It’s not enough to build a model in isolation. Employers want to see that you can deploy it, scale it and deliver value in a real-world setting. 

Kate McDermott explained: 

“The green flags that I’m looking for… is the productionisation capability…The businesses that we tend to work with are those that are particularly agile and want to scale quickly and build products and take them to market, and so we need people that can demonstrate they have built production-grade systems and frameworks.” 

The strongest portfolios tell the whole story – from identifying a business problem and building a solution to implementing it and demonstrating the results. That’s why portfolio-based learning is becoming increasingly important, giving employers clear evidence of how candidates apply their skills to real-world challenges. 

AI makes strong foundations even more valuable

As AI tools become more accessible, the value of deep expertise continues to grow.

Robert Hardman described AI as a force multiplier.

“AI is a force multiplier. It will make somebody who is highly skilled and experienced 20, 50, a hundred times more productive, more effective. At the same time… it will make somebody who isn’t as talented think they are talented, and make them two to three hundred times more risky for an organisation.”

Professionals with strong analytical skills and good business judgement get even more out of AI because it helps them work faster and more effectively. They know how to question the results, spot what matters and make confident decisions.

Hardman’s own approach reflects this mindset.

“Never ask AI for an answer. Ever. I always ask AI, how do I find the answer? What should I be looking for?”

Recruitment processes are evolving in parallel.

Kate McDermott noted that technical assessments have become a central part of hiring.

“The technical screenings are back. They kind of disappeared for a while. They are back, and they are rigorous.”

Employers expect candidates to be confident using AI tools, while also understanding the statistics and reasoning behind the results. They want people who know when to trust AI, when to question it and how to explain their decisions. 

What this means for aspiring data scientists

The hiring code emerging from the discussion is remarkably consistent.

Employers want to hire professionals who can:

  • Connect technical work to measurable business outcomes.
  • Demonstrate production-ready, deployable solutions.
  • Explain and defend model decisions with statistical confidence.
  • Communicate effectively with both technical and non-technical stakeholders.
  • Show curiosity, adaptability and a commitment to continuous learning.

McDermott also highlighted an encouraging trend for professionals entering the field: organisations are increasingly hiring for potential. They know AI is changing quickly, so they value people who can adapt, learn fast and understand the business.

As she explained:

“Companies know that they can’t go and find 10 unicorns… people will more often than not hire growth.”

The strongest CVs do the same. They show the business impact of your work, highlighting the results you achieved, the problems you solved and the role data played before listing the tools you used.

Preparing for the hiring market ahead

The message from employers was clear. The strongest candidates combine technical skills with business understanding, practical problem-solving and the confidence to communicate their thinking.

The Cambridge PACE Data Science Career Accelerator is designed around these same priorities. Through more than 20 practical projects and a live Employer Project, learners tackle real business challenges and present their recommendations in a consultancy-style pitch. By the end of the programme, they have a portfolio that showcases both technical skills and commercial thinking.

Ready to build the skills employers are looking for? Download the brochure to learn more about the curriculum, employer projects, learning experience and career support in more detail.

New Programme Takes Learners From Any Starting Point to AI Engineer with Three Pathways

Posted on by Marisa Crous

There’s a strange gap sitting in the middle of the AI boom. Almost every company now uses AI in some form, but only a small number have people on staff who can actually build it, deploy it, and keep it running once real users and money depend on it.

We built this programme specifically to close that gap, training engineers who can take AI systems from working demo to production reality.

We’ve just launched the Cambridge PACE AI Engineering Career Accelerator in collaboration with the University of Cambridge Professional and Continuing Education (Cambridge PACE), designed to help you become an AI engineer, whatever your starting point.

Choose the pathway that’s right for you

Most upskilling programmes make you fit their starting line. We built this one differently, for a field as varied as AI engineering, where the people who need these skills range from analysts taking their first steps in code to senior engineers who’ve shipped production software for years. 

So instead of one entry point, there are three:

🔹 Track 1: Non-coder ➜ AI Engineer – 36 weeks | Starts 26 October 2026 

🔹 Track 2: Coder ➜ AI Engineer – 28 weeks | Starts January 2027 

🔹 Track 3: Software Engineer ➜ AI Engineer – 16 weeks | Starts January 2027

You don’t need to already be a developer, or come from a particular background, to become an AI engineer. What you need is the drive to build the capability, and a structured pathway that starts at the beginning and takes you all the way. Whether you’ve never written a line of code or you’re already building production software, the programme starts from where you are and finishes in the same place: a working AI engineer.

Placement comes from a short diagnostic conversation with an Enrolment Advisor, who places every applicant based on real experience and capability – so each learner starts exactly at the point that matches their skills, ready to build from there.

Whichever pathway you enter on, you leave the same place: an AI engineer, with a Cambridge PACE credential and a portfolio that proves it.

Built around what employers actually check: can you ship it

This programme teaches engineering discipline, because that’s what production work actually demands. You’ll gain production AI engineering skills employers need, including working with large language models (LLMs), retrieval-augmented generation (RAG), AI agents, and LLMOps – plus AI security hardening, responsible AI governance, and inference cost engineering, the kind of cost-and-scale thinking that separates a working demo from a system a business can actually run.

Every learner works with the tools AI engineers use on the job day-to-day, including but not limited to GitLab, Docker, FastAPI, Postgres with pgvector, Langfuse, and the Claude API.

And every learner finishes with an employer-led capstone project: building a production AI system to a real employer-provided brief, presenting it to employer partners on Demo Day, and publishing the finished artefacts publicly. 

It’s assessed as a team and individually, so what ends up in your portfolio is unmistakably your own work, ready to speak to confidently in any interview.

The support continues well beyond graduation

One-to-one career coaching starts in week one and runs throughout the programme. You’re paired with a success manager and course facilitators throughout, and the programme is backed by the FourthRev Guarantee: achieve your agreed career outcome within 12 months, or receive up to six additional months of career coaching at no extra cost.

Not sure which pathway is right for you? That’s exactly what the Enrolment Advisor call is for – they’ll help you find it together. Download the brochure for the complete curriculum, fees, and admissions criteria.

Gitahi Wamugi on Leading AI Beyond the Tech

Posted on by Marisa Crous

Gitahi Wamugi advises C-suite and senior executives across 68 enterprise customers as a Client Solution Executive in Data and AI at IBM. He completed the LSE AI Leadership Accelerator and spoke to us in a recent AMA webinar about how it changed the way he advises clients.

“The first question is always why?”

For Gitahi, that question sits at the centre of every AI conversation. He has worked at IBM in the UK for about five years. Before his current role, he spent time as a customer success manager, helping clients get value from the software they had already bought.

About three years ago, generative AI changed the shape of that work. Gitahi saw a wave of product uptake across his customers, and a growing problem behind it: many organisations were struggling to get AI into production. Boards wanted to move quickly, yet many enterprises were still running legacy systems. 

That left him with a question: how could he explain to customers that no single tool would shortcut modernisation, and that progress depended first on a conversation about business value and managing change? 

That question sent him looking for a different kind of learning.

Choosing leadership over another technical course

IBM gives Gitahi plenty of technical training, and he is clear that technical qualifications still matter in his field. What he was missing was a leadership view: how to help customers turn strategic direction and expected business value into results.

“I really wanted a course that would speak to the business value,” he says.

He researched the market and was initially sceptical. With so many AI programmes available from leading universities, he wondered whether he would be drawn into an academic view of AI when his own experience came from implementation in the field. He reviewed the LSE AI Leadership Accelerator in detail and spoke with the enrolment team, which helped him decide. 

Two things stood out. First, the programme focuses on leadership, organisational change, and the business impact of AI. The second was the leadership coaching that runs alongside it.

“For me, that was added value. That was a decision maker. I didn’t see that in any of the other courses that I researched on that were available in the market.”

He already knew LSE, having completed a three-month LSE course on AI governance and ethics the year before.

Learning that showed up in client work

Gitahi has a technical background spanning enterprise data and AI, although his work focuses on solution strategy, transformation and business value rather than software development. He found the Accelerator gave enough grounding in the technical side of AI for learners without an IT background, while pointing everyone towards the leadership frameworks and methods they would need to keep building their knowledge.

What mattered most to him was how quickly the learning moved into his day-to-day work. Because his role is to help customers get value from AI, he began applying ideas from the group discussions and lectures straight away, and his assignments drew on use cases from his own client work.

He started building his own playbooks and looking at customers in new ways. One example came from industry reports on where chief data officers saw the biggest gaps: data quality, governance, and a repeatable way to connect AI to the right data.

“So I was able to develop a playbook for identifying and accelerating suitable use cases towards value within a 30-day framework.”

The programme also shifted how he reads a boardroom. The module on the psychology of change, including the idea of affinities and aversions, made him look again at the people behind each decision, and at his own reactions to change.

“In a lot of cases, I’m beginning to see that the human psychology of the people who have power sitting around that table might be the most important thing to driving the change that you want to achieve.”

C-level leaders bring their own objectives, he says, and many share the concerns anyone else has: whether AI will affect their role, and whether they can deliver what the board expects.

Governance as a continuous process

Gitahi came to the programme with a data transformation background, and in earlier client conversations, governance had usually meant governing AI models, from the top down. The Accelerator showed him a further gap: organisations also need to govern the data their models draw on, from the bottom up.

“It’s not a goal, it’s not a project. It’s not a place that you get to – it’s a continuous process. You have to continually manage that.”

He sees this as central to the question many enterprises now face, which is how to move AI into production. His answer is governed data products and assets, with risk and compliance managed at the foundations. The programme also gave him a way to make that case in commercial terms.

“You can actually translate that into an ROI calculation for your board and say, this is the cost of doing nothing.”

The people around the table

Gitahi learned alongside senior professionals from a range of sectors, and he found the breakout sessions especially useful.

“This was very valuable, because you got to hear from people of different domains, what was important to them.”

He also noticed that peers without an IT background earned high distinctions on the project assignments, which he puts down to them approaching the concepts without the biases that experience can bring. In his view, he had to navigate his own.

His leadership coach helped him see his client conversations from the outside.

“It was really good to have the conversations with a coach who would listen to me and mirror back what I had said and to hear what my clients hear when I speak.”

Coaching on the Accelerator runs for 12 months, so Gitahi’s sessions continue after the programme ends.

Making the time

Gitahi works hybrid and his days are long. He is honest that the programme took effort.

“I still had to take time. I had to find time to work over weekends, late nights.”

In a typical week, he set aside about an hour in the evening for reading, then caught up on weekend mornings. Having recently finished an MSc, he wanted something shorter and more focused, and the six-month format suited him.

Looking back, his one piece of advice to himself would be to go deeper. “It’s helped me also see areas of potential growth for myself.”

The LSE name in the room

Gitahi does not lead with his qualifications in meetings, but he knows some clients look him up first.

“But there are certain meetings that you go into where people will do their homework about you before you walk into the room.”

In those settings, he says, the LSE name carries weight at board and executive level, and it opens the door to a more personal, intellectual discussion that helps him understand what the other person values.

What comes next

Gitahi sees a lasting need for change management as AI reshapes roles and workforces, even in organisations that have led transformation before. He is positioning himself to grow further into a trusted advisory role, helping organisations translate data and AI strategy into measurable transformation, while continuing to deepen his expertise in AI, governance and organisational change.

His advice to senior professionals under pressure to lead AI is to start with purpose rather than fear of missing out.

And on the decision to apply, he recalls the question he asked himself:

“I looked at the rate of change in the industry and asked myself, am I willing to wait another year before I make this kind of a decision?”

“And having made the decision, I don’t have any regrets.”

Lead AI in your organisation with the LSE AI Leadership Accelerator. Over six months online, you’ll work through four courses (Applying AI in Organisations, From Vision to Value, AI and Change Leadership, and AI for Executive and Team Performance) and produce four board-ready deliverables, supported by 12 months of 1:1 leadership coaching.

Download the brochure here.