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Company Information
The company I worked at is called Grant Thornton Bharat LLP. Their website is https://www.grantthornton.in/. The company operates in the professional services industry, specifically within the accounting & tax & business advisory sector. The company is very large-sized and operates in over 150 countries with a US$8B+ revenue and has 80,000+ employees. “A member of Grant Thornton International Ltd, Grant Thornton Bharat is a leading professional services firm in the country. A truly Indian Firm with global connections - we work with businesses and government across industries and sectors, providing assurance, consulting, tax, risk and digital and technology transformation services.”: https://www.grantthornton.in/en/about/.
Position Information
I worked as a Software Engineer (intern) for 4 months, from May 2024 - Aug 2024, specifically in the dGTl (AI First Transformation) team. I worked on representing data in various means and applying mathematical models and neural networks to various datasets. I learnt how to run DBMS operations across millions of rows, and this made me a much better engineer because I was forced to think about the greater impact my work was having. Grant Thornton Bharat is headquartered in India.
Project Context
Most of my work sat in data and applied machine learning. I represented large datasets in different ways and applied mathematical models and neural networks to them. A recurring theme was making database work hold up across millions of rows, and learning to reason about the downstream effect of decisions at that scale. The point of the role was to turn messy data into something the wider practice could act on.
Technologies Used
- Programming languages: Python, SQL
- Data & ML: pandas, numpy, common neural network frameworks
- Databases: relational databases at scale
- Development tools: Visual Studio Code, Git / GitHub, notebooks
- Methodologies: Agile, Scrum
People
- Team: the dGTl (AI First Transformation) practice
- Ways of working: paired with data and consulting colleagues on shared datasets