Protein Design Data Scientist (ML & Modelling)

Genmab; Utrecht; Hybrid; EUR 60,000 - 89,000; Full time

Benefits offered by this job

Pension

Health insurance

Paid time off

Employee support programs

Job summary

Genmab is seeking a Data Scientist with a strong machine learning background and experience in protein structure or protein-protein interaction modelling to join our Discovery Data Science department in Utrecht. You will work at the interface of machine learning, molecular modelling, and experimental antibody discovery to design and evaluate potential antibody binders against therapeutic targets.

This is hands-on scientific work with collaborators across computational and laboratory science.

Qualifications

• PhD with hands-on ML experience applied to protein structures or protein interactions.
• Postdoc experience not required; up to two years is typical.
• Excellent communication skills in English.
• Strong Python and Linux/CLI skills.
• Experience writing reproducible scientific code.

Responsibilities

• Analyse protein structures and binding surfaces for de novo binder design.
• Benchmark generative ML models for protein complexes and assess their performance.
• Develop computational workflows to filter, rank, and prioritise designs for experimental testing.
• Collaborate with data scientists, computational scientists, and lab colleagues.
• Write robust, reusable scientific code and ensure maintainability.
• Communicate methods and results clearly through discussions, visualisations, and reports.

Skills

Python

Machine learning

Data analysis

Scientific programming

Git

Education

PhD in Computational Biology

Tools

CLI

HPC

Git

Job description

Genmab is seeking a Data Scientist with a strong machine learning background and experience in protein structure or protein-protein interaction modelling to join our Discovery Data Science department in Utrecht. You will work at the interface of machine learning, molecular modelling, and experimental antibody discovery to design and evaluate potential antibody binders against therapeutic targets.

This is hands-on scientific work with collaborators across computational and laboratory science.