Data Scientist, Digitalisation & Process Optimisation

Data Scientist, Digitalisation & Process Optimisation

1. Model Development & Deployment: Design, build, and maintain machine learning and optimisation models for real manufacturing problems (e.g., soft sensors, quality prediction, set‑point optimisation, scheduling solvers).

2. Control Integration: Validate and deploy digital PID tuning recommendations; integrate analytics outputs with DCS/PLC and plant historians for closed‑loop or advisory control.

3. Simulation & Digital Twin: Develop and maintain Aspen (or equivalent) steady‑state/dynamic models; support digital twin use cases for scenario analysis and process optimisation.

4. Data Engineering for Operations: Ensure data availability, structure, and quality across historians, MES, LIMS, ERP; build reliable pipelines for near real‑time and batch analytics.

5. Change Management & Adoption: Partner with production and maintenance to operationalise models (MLOps), verify impact, and embed...