How the Cambridge PACE Data Science Career Accelerator Helped Sanya Land a Role at John Laing
- Marisa Crous
When Sanya Setia joined the Cambridge PACE Data Science with Machine Learning & AI Career Accelerator, she wasn’t simply aiming to earn another qualification. Her goal was much bigger: strengthen her technical abilities, grow her confidence, and stay relevant in a world where data and AI were rapidly transforming how organisations operate.
At the time, Sanya was working in Cambridge as a Business Intelligence Developer at a healthcare technology scale-up. Around her, conversations about automation, machine learning and artificial intelligence were becoming more frequent — and she wanted to be part of that shift.
“It was almost criminal not to know what’s happening around me.”
Over the next seven months on the Career Accelerator, Sanya built a portfolio of practical projects and developed a deeper technical skill set. Soon after graduating, that experience helped her secure a new opportunity at John Laing, a leading infrastructure investment company in London. She joined the firm as a Business Information Analyst, doubling her previous salary in the process.
We spoke with Sanya during a recent Ask Me Anything event hosted by Cambridge PACE and FourthRev, where she shared insights about balancing study with work and how the programme helped her progress into a new role.
Before the programme: Building credibility in data science
Sanya already had experience working with data, but she wanted to deepen her understanding and stay ahead of developments in AI and machine learning.
“I wanted to disrupt my workplace using the cutting-edge.”
Her search focused on finding a programme that combined academic credibility with hands-on learning. She wanted something rigorous enough to explore data science in depth, while still grounded in practical applications.
“I decided to do a programme that was backed by a university that’s well-renowned and also was long enough to go into the depths of the topic itself.”
That search ultimately led her to the Cambridge PACE Data Science with Machine Learning and AI Career Accelerator.
Learning by doing: Projects that accelerate real growth
From the outset, Sanya expected the programme to be challenging — but she welcomed that challenge.
Balancing full-time work, personal commitments and study required careful time management. Scheduling dedicated study sessions helped her stay consistent throughout the programme.
“I had dedicated time slots in my calendar for self-study and classes…I used it extensively, and this kept my expectations set.”
A key element of the learning experience was the project-based structure of the curriculum. Each week introduced new concepts that learners could immediately apply, reinforcing their understanding through hands-on work.
“…we were learning on the go and applying on the go, which meant everything I learned was solidified.”
As her skills progressed, Sanya began experimenting with advanced tools and models such as Hugging Face and FinBERT, building small applications she could refine independently. “My achievements were my projects that I was doing week in and week out because I could always go back to them and see how I approached a problem and how I could solve it and make it fit into my new project,” says Sanya.
Creating a portfolio that demonstrates real skills
Over the seven-month programme, Sanya’s weekly work evolved into something far more valuable: a comprehensive portfolio of data science projects.
By the time she graduated, she had developed a substantial GitHub repository featuring data visualisations, machine learning models and AI-driven solutions. These projects became powerful evidence of her technical ability and problem-solving skills.
“This programme gave me an immense portfolio of projects on every machine learning topic that I have, and to put that on my GitHub, to put that on my website, and to be able to showcase that to my new employers.”
From uncertainty to confidence in Python and AI
Beyond technical skills, the programme also helped Sanya develop greater confidence in her abilities.
“I definitely feel more confident in Python programming.”
Before starting the course, she wondered whether someone without a traditional computer science background could succeed in such a technical field. However, the combination of mentorship, community support and structured learning helped change that perception.
She credits much of her growth to the support system around her — including tutors, industry mentors, success managers and fellow learners. “They never made you feel like there is a bad question, or what you’re asking is too basic,” says Sanya.
By the end of the programme, Sanya felt far more comfortable navigating the world of AI and data science.
“The programme gave me an upper hand and the self-confidence to know that knowledge about AI and technology isn’t out of reach anymore.”
A new opportunity at John Laing
During the programme, Sanya made the bold decision to leave her previous role after realising it no longer offered enough progression.
Working closely with her FourthRev Career Coach and Cambridge PACE industry mentor, she refined her CV, strengthened her LinkedIn profile and learned how to present her project portfolio effectively to employers.
This preparation paid off.
“As a result of that, I did get placed in a job right after my course. A big part of it, in terms of my CV and my confidence, was having done this course, where I knew what I was talking about.”
Shortly after completing the Career Accelerator, Sanya accepted an offer from John Laing, stepping into a new role with significantly increased responsibility and double her previous salary.
Advice for future learners
Looking back on her journey, Sanya encourages prospective learners to approach the programme with curiosity and commitment.
“…my recommendation is to prepare well, prepare in advance, know what you’re getting into, and go in with… the learning mindset.”
For her, the key is focusing on genuine understanding rather than simply completing the course. “Don’t do it for the sake of finishing. Do it for the sake of learning something new,” says Sanya.
And for those feeling stuck in their careers or unsure about the next step, she offers this perspective: “…I would recommend it to someone who is stuck somewhere, or feels saturated somewhere, and wants to make that leap to implementing machine learning or AI solutions.”
Explore how the Cambridge PACE Data Science Career Accelerator can help you take the next confident step in your career – just as it did for Sanya.
