Description:
We’re supporting a leading international tech-driven organisation with deep expertise in probabilistic modelling, pricing algorithms, and predictive analytics. Their London-based Quant Engineering team is building a modern, in-house forecasting and pricing engine to support real-time decision-making in a dynamic and high-volume domain.
You’ll join a collaborative team of engineers, researchers, and data scientists working to advance and deploy predictive models into scalable production systems. This is a high-impact role ideal for someone who thrives at the intersection of modelling and software engineering.
What You’ll Be Doing
- Turn prototype models into clean, reliable, and well-tested production services.
- Build and maintain APIs that enable access to pricing and forecasting models at scale
- Work closely with Quant Researchers to implement mathematical ideas in performant Python code
- Design model libraries and tooling that prioritise flexibility, reproducibility, and runtime speed
- Contribute to the long-term design of the quantitative platform and its integration into broader engineering systems
- Promote software best practices around code quality, deployment, documentation, and observability
- Explore and evaluate new modelling approaches or statistical methods that could benefit the team’s roadmap
- Collaborate across departments to ensure that models are impactful, interpretable, and accessible to wider teams
What They’re Looking For
- Commercial experience in machine learning engineer, model engineering, quantitative development, or a related field
- Strong Python skills with a solid grounding in object-oriented principles and software architecture
- Experience with numerical and scientific computing libraries such as NumPy, SciPy, and Numba
- Familiarity with building production-grade APIs and deploying statistical models into live systems
- Knowledge of best practices across version control, CI/CD, linting, unit testing, and deployment workflows
- Strong written and verbal communication skills, with the ability to explain complex ideas to non-specialists
- Curiosity and drive to experiment, learn quickly, and contribute beyond your comfort zone
- Bonus: Experience with Docker, Kubernetes, AWS, or orchestration tools like Airflow
- Bonus: Background in sports data, betting, or other real-time probabilistic modelling environments (*TO BE CLEAR - NOT MANDATORY!*)