Dhruv Mehndiratta

I did my undergrad at McGill, in Mathematics and Economics with a minor in Political Science, and stayed in Montreal long enough to get attached to it. The MA in Economics, plus a Graduate Diploma in Computational Data Analytics, came after that at the University of Waterloo, mostly because I wanted the technical side (the programming, the modeling, the parts of empirical work that math and econ degrees don't always teach directly) to catch up with the theory.

That combination, theory from one program and tools from the other, is roughly how I'd describe my background: an economist by training who is comfortable enough in Python, R, and Stata to build the thing rather than just specify it.

Most of my work falls into two buckets: causal inference research, and applied financial and data analysis.

On the research side, I've worked on a CIGI project studying how bitcoin prices affect mining energy consumption, using causal graphs to separate the actual price effect from confounding factors across a panel of over 100 countries. Recently worked with my supervisor at Waterloo on AI-era wage premiums, looking at how job postings price AI skills across the UK, Canada, and Australia. A good chunk of that work is less glamorous than it sounds: cleaning and validating a dataset of roughly 8,000 postings, which is where the actual surprises tend to show up.

On the applied side, I spent time as a Financial Analyst and Officer with the Ontario Ministry of Health, where the work was less about modeling and more about finding money: reconciling records and building the spreadsheets that supported recovery of over 7 million dollars in public funds.