[template]
This page captures the shape of my Grant Thornton engagement: the role, the team and what I owned.
The role
I worked as a software engineer intern at Grant Thornton, part of the dGTl (AI First Transformation) practice. My focus was applied data and machine learning work, mostly turning large datasets into models and analysis the consulting side could use.
In one line: I built and ran the data plumbing behind the practice’s AI work.
The team
- Team: dGTl (AI First Transformation)
- Report line: my practice lead
- Collaborators: data engineers and consulting colleagues
- Engagement model: project-based work on client and internal datasets
Scope and ownership
| Area | What I owned |
|---|---|
| Primary focus | data representation and applied ML models |
| Technology | Python, SQL and common ML frameworks |
| Delivery | iterative work in short cycles with regular reviews |
| Stakeholders | internal data teams and consulting leads |
What I was responsible for
- Preparing and representing large datasets so they were usable for modelling.
- Building and testing mathematical models and neural networks against those datasets.
- Making database work hold up as row counts grew into the millions.
- Documenting what I built so colleagues could pick it up.
The impact
The result I care about most was learning to reason about scale: a choice that looks fine on a small sample can fall over on millions of rows. The part I am proudest of was making that kind of work repeatable rather than one-off.