By Sandeep, Level 7 AI Data Specialist Apprentice at the Science and Technology Facilities Council
As part of our Level 7 AI Data Specialist apprenticeship, we're proud to support professionals like Sandeep who are using artificial intelligence to bring meaningful change to complex project environments. In this blog, Sandeep shares why he joined the course, how he’s applying his learning, and what he hopes to achieve by blending technical AI knowledge with real-world project delivery in the science and technology sector.
Why I chose the Level 7 AI Data Specialist course
I chose the Level 7 AI Data Specialist apprenticeship because I saw a clear opportunity to bring real value to the way we plan and deliver research and experimental projects in the physics and space domain. My role involves supporting Project Managers and Work Package Managers with scheduling, monitoring, and controlling tasks and it’s clear there’s significant scope to improve how we handle data, automate repetitive processes, and make better-informed decisions.
There’s huge potential for AI and data-driven tools to support early risk identification, resource forecasting, and reducing the admin burden on project teams. I wanted to develop the skills to build and apply these kinds of tools in practice and this course felt like the right way to do that.
What I’ve learnt so far and how I’m applying it to my role
The course began with the fundamentals of AI and machine learning, which gave me a solid grounding in the core concepts and techniques. More recently, we’ve explored deep neural networks which I’ve found particularly interesting. It’s made me consider how we might use these models to identify patterns in historical project data, or even predict issues like schedule overruns before they occur.
Even at this early stage, I’ve started applying some of the learning. For example, identifying manual processes that could be automated, or using structured data more effectively to spot trends and anomalies. It’s also changed how I think about data not just as something we collect, but as something we can actively use to improve project delivery.
What’s next
Looking ahead, I’m keen to start developing small AI-based tools that we can test within our team. These might include predictive models for scheduling risks or using natural language processing to extract insights from technical reports and risk logs. I also see real potential in building dashboards or digital assistants to support stakeholders with day-to-day monitoring and reporting.
Ultimately, my goal is to help create a more intelligent, data-aware project environment where decisions are evidence-based, and where we’re making the most of the data we already have. As I progress through the course, I hope to bridge the gap between technical AI knowledge and practical project delivery in a way that genuinely benefits the organisation.
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