Design and deploy platform features, data pipelines, automations, and internal tools that improve Allocator's data aggregation, analytics, and reporting workflows
Use AI coding assistants as core daily tools; we value problem-solving ability and execution speed over memorising syntax
Build agentic workflows and AI-driven features into our platform and internal tooling, applying LLMs and orchestration frameworks where they deliver real leverage
Analyse workflows across data collection, fund reporting, and client delivery, identifying improvement opportunities and implementing practical solutions
Move from concept to prototype to production efficiently. Launch MVPs, track performance, and iterate based on client and internal feedback
Collaborate with Product, Data, and Client Success teams to understand requirements, demonstrate solutions, and support adoption
Establish performance baselines, implement changes, and document measured impact for every feature shipped
Lead discovery sessions with Product and Data teams, documenting success criteria, technical specifications, and trade-offs clearly
Develop release notes, setup guides, and onboarding materials accessible to non-technical colleagues and clients

Bachelor's degree (Master's preferred) in Computer Science, Applied Mathematics, Engineering, Statistics, Physics, or a related quantitative discipline
First-Class or Upper Second-Class Honours (2:1 or above) from a UK or internationally recognised university, or equivalent (e.g. GPA 3.5+/4.0)
Strong written and verbal communication, capable of producing technical specifications, presenting demonstrations, and translating between technical and business contexts
Analytical mindset with the ability to decompose complex, ambiguous challenges into actionable steps
Programming foundation in Python, SQL, or JavaScript (academic projects, internships, or capstone work)
Daily working fluency with AI development tools (ChatGPT, Claude, GitHub Copilot, Cursor), used as core tools rather than occasional aids
Bias for action; prefers experimentation and data-driven iteration over extended planning
Desirable: Hands-on experience building with LLMs, prompt engineering, or AI agent frameworks in a production environment
Background in data analysis, process optimisation, or computational modelling
Internship or work experience in fintech, asset management, consulting, or technology
Familiarity with or interest in alternative investments, fund data, or institutional finance

Professional development budget (e.g. CFA, CAIA, or other relevant certifications)
Hybrid working from our Shoreditch-based London office (1 to 2 days in office per week minimum)
Private medical cover
Pension plan contributions
Regular team socials and events

Allocator

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