Machine Learning Data Scientist - BioML

Machine Learning Data Scientist

Germany (Remote UK/EU OR +/- 2hr CET)

€60,000–80,000 + stock options

Are you interested in working on applied research within AI native Biology, with opportunities to publish in leading journals in collaboration with industrial and research partners?

Here you will join a pre-seed start up where you will take ownership of your work, contributing heavily to research decisions, where your ideas carry weight and your contributions help shape the future direction of the company. The position is fully remote with occasional travel within Europe.

What you’ll do

Develop hybrid mechanistic/ML cell response models that predict fermentation outcomes (growth, titer, yield, productivity) from process and sensor data. This includes fusing time-series signals from process analytical technology (Raman, NIR, dielectric spectroscopy, dissolved oxygen, off-gas) with offline analytics.

You'll build mechanistic backbones (e.g., kinetic/ODE models, genome-scale metabolic models) that constrain learning and generate synthetic training data. You'll also develop machine learning surrogate models for computationally costly mechanistic simulations and quantify uncertainty so that models can be trusted in high-stakes production settings.

What you’ll need

• PhD in machine learning, bioengineering, computational biology, or a related field, or a Master’s degree with 3+ years of relevant industry experience.
• Hands-on experience building models of dynamical systems or time-series data, ideally combining mechanistic and data-driven approaches.
• Demonstrated evidence of research contributions, including (but not limited to): peer-reviewed journal or conference papers, conference attendance, open-source contributions.

About the company

An early-stage biotech using AI and experimental biology to improve how biological production processes are developed and scaled. Their approach combines lab-generated data with predictive models to help teams understand process performance, reduce costly experimentation and move more efficiently towards commercial manufacturing.

Reach out to mason@ct-19.co.uk for more information.