Data Scientist, Digitalisation & Process Optimisation

Main Accountabilities:

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 solutions into standard work.

6. Performance Visibility: Deliver intuitive dashboards and alerts that expose leading indicators, constraints, and improvement opportunities.

7. Sustain & Improve: Monitor drift, retrain models, and continuously improve based on new data and evolving process conditions.

Expected Deliverables: • Measured OEE gains and throughput/capacity improvements • Digital PID tuning outcomes for reactors/critical loops with validated benefits • Predictive maintenance models reducing unplanned downtime • Automated scheduling or optimisation tools that respect plant constraints • Real‑time plant performance dashboards with actionable insights

Additional Information: • Travel to plant sites as required for trials, commissioning, and operator training. • Role involves time in operating areas; adherence to all EHS requirements is mandatory.

Working Relationships: • Internal: Production, Maintenance, Engineering, EHS, Supply Chain/Planning, IT/OT • External: Vendors/consultants for DCS, historians, and simulation; academic/industry partners as needed.

Success Measures (12–18 Months) • Quantified improvements in throughput, yield, quality, or energy with validated financial impact • Stable deployment of at least two production-grade analytics/optimisation solutions integrated with plant systems • Robust MLOps/monitoring in place (drift detection, retraining cadence) • Positive feedback from operations on usability and adoption

Why Synthomer?

We are ambitious!
We have grown significantly – both organically and inorganically. We are a FTSE 250 listed company, 22% of our revenue comes from newly commercialized products, and we’re recognized in the top-quartile for chemicals manufacturing safety.​

We believe in high challenge, high support!
We are keen to let you contribute in real roles from day 1. We expect a lot, but offer a lot, too. This includes onboarding, induction and learning events, networking opportunities, mentoring and personal development planning. So, be up for an inspiring long-term career adventure.​

We personalize our approach to development!
At Synthomer, you won't find generic career tracks or development programs but rather a one-size-fits-one approach to employee development. We'll partner with you to ensure you have the right experiences that build your capabilities and accelerate your career growth.​