Sep 2022 - Present
McKinsey & Company
Business Analyst → Senior Associate Strategy, Transformation & Build
Economist by training, advisor and investor by trade, builder by instinct.
Hi, I'm Rana. I thrive on the kind of challenge that pulls me into a flow state: when things are messy and the stakes are high. My instinct is to make sense of the system first, then work my way through it iteratively: testing, building, learning, and looking for the more original answer. I trained as an economist, spent a couple of years in the middle of post-pandemic financial markets at a hedge fund, then consulting taught me how to really influence people. I move between the big picture and the detail, and I'd take first-principles thinking over a playbook any day. Outside work, you'll find me at a rave, off travelling somewhere new, or out biking on the trails.
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01 / 04Employers
Sep 2022 - Present
Business Analyst → Senior Associate Strategy, Transformation & Build
Sep 2020 - Jul 2022
Economic Analyst → Multi-Asset Analyst Alternative Investment
Education
2017 - 2020
BSc Econometrics & Mathematical Economics (EME) First-Class Honours Premchand Prize: highest mark in senior-year Monetary Economics Top 5% in Quantitative Finance and Maths
2016 - 2017
A-Levels A*A*A*: Mathematics, Further Mathematics, Biology Completed the two-year course in 9 months A-Level Student of the Year
McKinsey
McKinsey
McKinsey
SPX Capital
Newspeak House / ACX
Manifest / LessOnline
WIP · lodestone.digital
A persistent AI operating system deployed at the project level - it ingests every signal, updates every output, and compounds quality each daily cycle, so the team can focus on being in the room and making decisions.
WIP · agent-orchestration tool
Reimagining Git's branch/merge model into a way to lead a team of AI agents - the way a consultant runs parallel workstreams. Git is great for backup and revert when an agent goes wrong, but it isn't built for leading a team.
The vision: see swim lanes of workstreams moving forward, branching, merging, with dependencies - run agents in parallel at scale and know where work depends and converges. Take Git data, show it differently, with extra signals layered on top.