- Company Name
- CPI
- Job Title
- Applied Data Scientist (Research Engineer – Digital Technologies)*
- Job Description
-
Job Title: Applied Data Scientist (Research Engineer – Digital Technologies)
Role Summary:
Lead the development and deployment of data‑driven solutions for complex materials within the Automation and Digital team. Leverage machine learning, statistical modelling, and cloud‑based computing to generate actionable insights for energy storage, sustainable materials, pharmaceuticals, and consumer goods. Drive innovation through integration of advanced automation, digital twins, and high‑throughput experimentation in a 24/7 robotic formulation laboratory.
Expectations:
- Cross‑disciplinary expertise in chemistry, physics, biology, or mathematics combined with advanced data science capabilities.
- Deep understanding of materials at molecular, atomic, or structural levels.
- Proven experience applying machine learning and high‑dimensional modelling to real‑world materials challenges.
- Ability to work on multi‑scale projects (1 month to multi‑year), across diverse sectors (batteries, nanotherapeutics, FMCG, food & feed).
Key Responsibilities:
1. Design, develop, and validate predictive models, ML/AI algorithms, and DOE strategies for formulation optimisation.
2. Automate data capture, preprocessing, and analysis using low‑code platforms, Python scripts, and cloud services.
3. Collaborate with technical leads to deliver client‑oriented solutions and provide strategic advice.
4. Contribute to the operation and enhancement of a 24 / 7 robotic formulation lab, integrating automation with data‑driven experimentation.
5. Create digital twins and simulations for battery manufacturing and other processes, combining physics‑based and data‑driven methods.
6. Conduct clustering, correlation, and other exploratory analyses to uncover causal relationships within large datasets.
7. Mentor and train team members on emerging techniques and technologies.
Required Skills:
- Programming: Python (pandas, scikit‑learn, TensorFlow/PyTorch), SQL, and low‑code/no‑code tools.
- Machine Learning: supervised/unsupervised learning, deep learning, model‑predictive control, DOE.
- Cloud Computing: AWS/GCP/Azure data pipelines, scalable compute, containerisation.
- Data Engineering: ETL design, data warehousing, data governance, automation of data workflows.
- Domain knowledge in materials science (chemistry, physics, biology) and application to energy storage, pharmaceuticals, or sustainable materials.
- Strong analytical, problem‑solving, and communication skills.
Required Education & Certifications:
- Bachelor’s or Master’s degree in Chemistry, Physics, Biology, Materials Science, Mathematics, or a related STEM field.
- Advanced degrees (PhD or equivalent) preferred.
- Certifications in data science, cloud platforms, or machine learning are a plus.
Sedgefield, United kingdom
Hybrid
09-12-2025