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)
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