We're looking for a Senior Quantitative Modeller to architect, build, and evolve our core modelling and analytics platform. This is a rare opportunity to join a fintech redefining institutional asset allocation at an early stage, with hands‑on ownership of the models and systems that power everything we do.
About Us
Allocation Strategy is a London-based fintech providing analytical infrastructure to institutional investors. Our mission is to set the industry standard in asset allocation analytics - transforming how investors make their most important portfolio decisions. Our subscription-based solutions combine cutting‑edge analytics with efficiency, helping clients do more with less.
What You Will Do
- Work directly with the Heads of Technology and Research to architect, build, and evolve Allocation Strategy's core modelling and analytics platform.
- Implement, validate, and productionise advanced models for asset allocation, risk, macro and scenario analysis, including data preparation, testing, calibration, optimisation, and maintenance as models and data evolve.
- Develop AI‑assisted research and modelling workflows, including data engineering, model design, evaluation, and monitoring.
- Translate theoretical research and investment logic into efficient, testable, and maintainable code.
- Rapidly prototype new modelling approaches and analytical tools, then harden them for production use.
- Build and maintain robust backend systems, data pipelines, and APIs supporting both internal research and client‑facing applications.
- Design, operate, and evolve cloud‑based data and model workflows, including orchestration, scheduling, monitoring, and failure handling.
- Contribute to system architecture, performance optimisation, and scalability as the platform and dataset complexity grow.
- Establish and uphold high engineering and research standards, including testing, documentation, and reproducibility.
- Collaborate closely with product and research stakeholders to ensure models are usable, interpretable, and commercially relevant.
Qualifications
- Master's or PhD degree in a quantitative discipline (or equivalent professional experience).
- 5+ years of relevant professional experience in quantitative modelling, software engineering, or applied research.
- Strong software engineering skills, with a track record of building and maintaining production‑quality systems.
- Expert‑level Python experience with numerical, statistical, or ML libraries (e.g. NumPy, pandas, PyTorch, JAX, TensorFlow).
- Deep understanding of statistical modelling, optimisation, or machine‑learning techniques.
- Experience building and operating backend systems and data pipelines in a cloud environment (e.g. AWS, GCP, or Azure).
- Experience validating, testing, and deploying quantitative or ML models.
- Comfortable working autonomously in a small, senior team, partnering closely with Founders.
- Strong analytical judgement, communication skills, and attention to detail.
- Experience with designing and refining large scale dynamic models.
- Experience applying AI or ML methods to real‑world, noisy financial or economic data.
- Strong awareness of the investment industry landscape, financial instruments, and market / economic events and news flow.
- Familiarity with finance and asset pricing theory and empirical methods, and the application of these concepts in the investment industry.
- Familiarity with cloud infrastructure, containerisation, orchestration, and CI/CD for model deployment.
- Experience in building institutional‑grade analytical tools or dashboards for sophisticated end users.
- Prior experience in a startup or early‑stage technology environment.
What we offer
Compensation
- Equity participation in a high‑growth venture
- Competitive base salary.
Other Benefits
- A collaborative, mission‑driven environment where your work directly shapes how institutional investors make multi‑billion‑dollar decisions
- Rapid career progression as the company scales
- Hybrid working model with international exposure
- Access to advanced technology and resources.
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