Applied Data Scientist; Research Engineer – Digital Technologies

About CPI

CPI helps make great ideas and inventions a reality. We’re a team of intelligent people using advances in science and technology to solve the biggest global challenges in healthcare and sustainability.

Through our incredible people and innovation infrastructure, we collaborate with our partners in industry, academia, government, and the investment community to accelerate the development and commercialisation of innovative products.

From health technologies and pharmaceuticals to sustainable food and materials innovations, we turn the entrepreneurial spirit and radical thinking of our people and partners into incredible impact that makes our world a better place.

CPI has an exciting opportunity for an Applied Data Scientist (Research Engineer – Digital Technologies) to join its established and growing Automation and Digital team within the Formulation Technology Team at our state‑of‑the‑art facility.

The ideal candidate will be a cross‑disciplinary thinker, combining expertise in chemistry, physics, biology, or mathematics with strong data science skills. In this role, you will leverage modern computational, statistical, and cloud‑based technologies to generate insights into complex materials, driving innovations across energy storage, sustainable materials, nanotherapeutics, and consumer goods. A strong foundation in materials at the molecular, atomic, or structural level is highly valued, along with a keen interest in the markets we serve—particularly energy storage, pharmaceuticals, and sustainable materials.

Experience in applying machine learning, high‑dimensional modeling, or data‑driven simulation on cloud platforms to address real‑world materials challenges will allow you to make an immediate impact.

Reporting to a Team Leader, you will leverage your existing skills in data science and be further developed in strategically relevant areas such as Machine Learning (ML), Artificial Intelligence (AI), predictive modelling, automation of data capture/processing, and ML‑driven Design of Experiments (DOE) methods. Using these skills across a combination of low‑code platforms, Python scripts, and cloud‑based technologies for scalable computation and data management, you will contribute to the delivery of innovation projects with our clients and support technical leads in providing advice and solutions to both internal and external stakeholders.

In addition, this role offers a unique opportunity to contribute to the development and operation of our 24/7 robotic formulation laboratory, integrating advanced automation with data‑driven experimentation to serve our markets.

• BATTERY 2030 +

Roadmap:

creating a digital twin for battery manufacturing that integrates data‑driven and physics‑based methods. It develops a cross‑chemistry data space for two technologies, Li‑ion and Na‑ion coin cells and redox flow batteries.

• EU project developing advanced sensing, monitoring and self‑healing mechanisms to self‑repair batteries, leading to the EU batteries of the future.

• LNP manufacture for supporting RNA vaccines and therapeutics.

• Facility for battery manufacture and analysis from the formulation through to the cell manufacturing served by collaborative mobile robots.

• Working with global leaders to apply model predictive control to their formulations.
• Using clustering and correlation analysis to determine cause‑effects or comparisons within datasets (e.g., developing models to predict soap stability, predicting best candidates for new ink materials).
• Supporting development of our automated platforms in service of our markets.
• Using machine learning techniques to analyze data to produce actionable insights.
• Developing soft sensors based on PLS and neural‑net models to predict slurry particle size and viscosity.
• Implementing Bayesian methods and/or state‑of‑the‑art methods for optimising the Design of Experiments approach.
• Developing a federated learning system to produce global models that enhance production in manufacturing.

• Supporting the planning and…