Data Scientist/ Optimisation Engineer
London - Onsite 2/3 days a week
6 month contract
Inside of IR35
£575 per day via Umbrella
We’re looking for an Operational Research–focused Data Specialist to join a high-impact decision intelligence team building optimisation and decision-support products used in live operations every day.
This role is less about pure algorithmic data science and much more about applying Operational Research, optimisation, and analytical modelling to complex, real-world problems at scale. If you enjoy turning messy operational challenges into mathematically grounded, production-ready solutions, this role is for you.
You might currently work in environments such as consulting (e.g. BCG X, Oliver Wyman, Bain, McKinsey) or product-led organisations in travel, logistics, marketplaces, fintech, or platforms (e.g. Airlines, delivery networks, booking platforms).
What you’ll do
- Design and implement optimisation and decision-support models to improve large-scale operational outcomes (e.g. Planning, scheduling, allocation, routing, capacity, trade-offs).
- Apply Operational Research techniques such as:
- Linear & mixed-integer programming
- Network optimisation
- Heuristics & approximation methods
- Work closely with software engineers and product teams to industrialise models into robust, scalable tools used in day-to-day operations.
- Build production-grade Python code, including clean abstractions, testing, and performance-aware implementations.
- Develop and maintain data pipelines to support optimisation and analytical workflows.
- Deploy models using modern cloud and orchestration frameworks, following CI/CD best practices.
Core skills & experience
- Strong grounding in Operational Research / Optimisation, rather than purely statistical or ML-heavy data science.
- Hands-on experience with mathematical optimisation (e.g. LP, MILP, heuristics).
- Proficiency in Python for analytical and optimisation modelling.
- Experience translating ambiguous, real-world operational problems into structured analytical models.
- Ability to balance solution quality, runtime, robustness, and real-world constraints.
Technical toolkit (experience with some of the following)
- Optimisation solvers (e.g. Gurobi, CPLEX, OR-Tools)
- Python libraries (e.g. Pandas, numpy, optimisation frameworks)
- SQL and data engineering fundamentals
- Cloud environments (AWS or similar)
- Version control (Git), testing, CI/CD
- Orchestration tools (e.g. Airflow, Dagster)