Project delivery and predictive modelling

Context

Another part of my background that is easy to miss on a simple CV is project delivery across technical and mixed-discipline teams.

In the Community Renewable Energy Project (CREP) work, I acted as a project manager intern while also contributing as a data analyst. That meant balancing coordination and modelling work instead of treating them as separate tracks.

What I did

• helped keep scope, assumptions, and project movement clear across a mixed-discipline team
• contributed directly to modelling and analysis work rather than only coordinating around it
• worked on machine-learning tasks where experimentation, evaluation, and explanation all mattered
• contributed to cloud and prototype delivery where usability and presentation were part of the value

Results

• helped mixed-discipline projects stay clear enough to keep moving instead of stalling around vague scope
• contributed to a team that placed first in the Accenture Tech Bootcamp
• shipped an AWS Amplify and serverless prototype rather than stopping at concept stage
• produced modelling and experimentation work that could be explained, reviewed, and used by other people on the team

What this shows

The pattern across these projects is consistent:

• I am comfortable contributing technically
• I also pay attention to scope, communication, and delivery discipline
• I tend to be useful when projects need both implementation and coordination

That is why I often describe myself as cross-functional rather than narrowly technical. In practice, I work best where technical detail still has to become something a team can plan, explain, and deliver.