Senior Data Scientist / ML Engineer – Simulation12-month contract | Fully Remote
We are working with an innovative technology business developing a digital twin platform for complex organisations.
The platform uses real-world operational data and event logs to simulate how organisations operate, allowing businesses to quantify the impact of operational changes before they are made.
We are looking for a Senior Data Scientist / ML Engineer to own the simulation and statistical modelling layer of the platform.
What you'll do
- Design and develop discrete-event and process simulation models in Python.
- Develop statistical and ML methods to fit and calibrate models against real operational data.
- Use event logs and timestamp data to infer processes, routing, capacity and durations.
- Design simulations and experiments to quantify operational changes.
- Build production-quality, reusable modelling systems.
- Work closely with data engineering and product teams.
- Communicate complex modelling outputs clearly to non-technical stakeholders.
Must-have
- Strong Data Science / ML Engineering experience with production-grade Python.
- Hands-on experience with process or discrete-event simulation, stochastic modelling or Operations Research.
- Strong statistical modelling skills, including estimation, calibration and validation.
- Experience working with real-world operational, event-log or timestamp data.
- Strong Python scientific stack: NumPy, SciPy and pandas.
- Good software engineering practices, including Git, testing and reproducible development.
Nice to haveExperience in complex operational environments such as:
- Aviation / airports
- Logistics / supply chain
- Transportation
- Manufacturing
- Healthcare
- Financial or professional services
- Consulting
Experience with process mining, queueing theory, Monte Carlo simulation, survival analysis or agent-based simulation would also be valuable.
We're particularly interested in candidates who have taken messy operational data, reconstructed how a real-world process works and built a quantitative model that can be used to support business decisions.