We are working with a well-backed, newly launched hedge fund in London, building a next-generation long/short equity platform that blends deep fundamental insight with advanced quantitative analytics.
This is a greenfield opportunity to help design and build the core research, risk and portfolio analytics tooling that will directly support the investment and risk decision-making process from day one.
You will work in a small, elite team, reporting into the CRO and partnering closely with senior investment professionals and the risk leadership, with real ownership and visibility over what you build.
The Opportunity
* Build and own core data and compute libraries used across research, risk and data science
* Design and implement research infrastructure including back-testing, portfolio optimisation and risk analytics
* Develop internal tools and applications used daily by PMs, analysts and the CRO
* Work across the stack (Python backend, analytics, some front-end exposure) to deliver production-grade solutions
* Take ideas from concept to production in a fast-moving, low-bureaucracy environment
This is not a maintenance role. You will be solving open-ended problems, making architectural decisions, and shaping how analytics and risk are embedded into the investment process.
Background
We are open-minded on background, but you will likely come from one of the following:
* Top technology firms (e.g. Google, Meta, Amazon, Microsoft) with experience building internal analytics, data or ML platforms
* Quant hedge funds or systematic trading firms, working as a Quantitative Developer or Research Engineer
* Research-led or data-heavy environments where engineering quality and problem-solving matter
Core Requirements
* Strong Python experience in production environments (pandas, numpy, scipy or similar)
* Experience building research, analytics or decision-support tooling
* Solid software engineering fundamentals: testing, version control, deployment
* Comfortable working with ambiguity and taking ownership of greenfield systems
* Strong communicator, able to work closely with non-engineering stakeholders
Nice to have
* Experience with optimisation, back-testing or risk models
* Exposure to ML frameworks or LLMs
* Some front-end experience (React / TypeScript)