We are currently supporting our client in their search for a Data Scientist to support the development and deployment of a digital twin platform for complex industrial process systems based on advanced separation and chemical engineering principles, along with analytical insights into the development of future technologies.
The successful candidate will work at the intersection of data science, process modelling, and chemical engineering to build predictive, physics-informed, and hybrid models that replicate and optimise real-world separation performance. This role is ideal for someone who enjoys translating process data into intelligent, operational decision-support systems.
Additional responsibilities include analysing experimental and pilot-scale data to support product development and performance characterisation.
Key roles and responsibilities:
- Data engineering & analytics – collect, process and analyse sensor and process data across lab, pilot and plant scales
- Digital twin development – support the development and validation of hybrid physics-ML models for complex separation and chemical systems
- Advanced modelling and optimisation – develop predictive models and apply optimisation algorithms to improve system recovery, energy and chemical usage, supported by scenario analysis
- Deployment and integration – collaborate with software engineers to deploy models into cloud or edge environments and support dashboard development and integration
- Cross-functional collaboration – translate engineering requirements into data science solutions and present findings to technical and non-technical stakeholders
Candidate requirements
- MSc or PhD in Data Science, Chemical Engineering, Computational fluid dynamics, Applied Mathematics, or related field
- 3+ years of experience in industrial data science or process modelling
- Strong programming skills in Python (NumPy, Pandas, SciPy, scikit-learn, TensorFlow/PyTorch)
- Experience with time-series modelling and multivariate data analysis
- Knowledge of statistical modelling, regression, classification, and optimization methods
- Knowledge of equilibrium and kinetics modelling
- Experience developing digital twins or hybrid modeling frameworks
- Familiarity with process simulation tools (e.g., Aspen, gPROMS, MATLAB)
- Experience with cloud platforms (AWS, Azure, GCP)
- Strong problem-solving
- Cross-functional collaboration skills
- Ability to manage multiple projects in a fast-paced R&D environment
- Excellent technical documentation and communication skills
It is essential that applicants have the right to work in the UK. Due to the nature of the role, successful applicants will be subject to satisfactory background checks including a basic DBS check.
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