Key Skills

Job Description

At Genmab, we are dedicated to building extra[not]ordinary® futures, together, by developing antibody products and groundbreaking, knock-your-socks-off KYSO antibody medicines® that change lives and the future of cancer treatment and serious diseases. We strive to create, champion and maintain a global workplace where individuals’ unique contributions are valued and drive innovative solutions to meet the needs of our patients, care partners, families and employees. Our people are compassionate, candid, and purposeful, and our business is innovative and rooted in science. We believe that being proudly authentic and determined to be our best is essential to fulfilling our purpose. Yes, our work is incredibly serious and impactful, but we have big ambitions, bring a ton of care to pursuing them, and have a lot of fun while doing so. Does this inspire you and feel like a fit? Then we would love to have you join us! The Role 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 join a multidisciplinary, international team working at the interface of machine learning, molecular modelling, and experimental antibody discovery. Your work will support our de novo design project, using machine learning models to design and evaluate potential antibody binders against therapeutic targets. This is a hands-on scientific role. Working with experienced colleagues who guide the project’s scientific direction, you will analyse target structures, apply and benchmark generative design and protein complex prediction models, and develop workflows to filter and prioritise computational designs. You will also analyse experimental validation results and help refine the computational approach as new data become available. The role offers the opportunity to further develop your expertise in (generative) machine learning and protein design while contributing directly to ongoing discovery research. You will work closely with computational and laboratory scientists and help build reproducible scientific software and workflows that can be reused across projects. We recognize that candidates may bring different combinations of machine learning expertise, biological knowledge and formal training. If you meet the core requirements but do not have every qualification or experience in every scientific area listed, we encourage you to apply! Responsibilities Analyse protein structures and potential binding surfaces to support de novo binder design and target assessment. Apply and benchmark generative machine learning models for protein complexes, assessing their performance, internal metrics, and limitations for the project’s scientific questions, and investigate potential improvements to the generative pipelines. Develop and improve computational workflows to filter, rank, and prioritise designs for experimental testing. Analyse experimental validation results with laboratory colleagues and use these findings to improve subsequent design and selection rounds. Develop or adapt machine learning models to support design evaluation and property prediction where relevant. Write robust, reproducible, and maintainable Python code, following software development best practices. Collaborate with data scientists, computational scientists, data engineers, and laboratory scientists to process and interpret molecular and experimental data. Communicate methods, results, and limitations clearly through project discussions, visualisations, reports, and presentations. Take ownership of agreed analyses and implementation tasks, seeking input from colleagues when needed. Requirements To succeed in this role, you bring scientific curiosity, a practical approach to problem-solving, and an interest in hands-on computational research. You also have: A PhD in Computational Biology, Bioinformatics, Biophysics, Machine Learning, Data Science, or a related discipline, with hands-on experience applying machine learning to protein structures or protein-protein interactions. Candidates nearing completion are welcome to apply, provided they expect to complete their PhD before the anticipated start date of 1 January or 1 February 2027. This role is suited to candidates joining directly after their PhD or with initial postdoctoral experience, typically up to two years. Postdoctoral experience is not required. The ability to communicate and discuss findings clearly to colleagues with different scientific backgrounds. Hands-on experience applying machine learning to protein structures or protein interactions, gained through doctoral research or another relevant industry/research setting. A solid understanding of machine learning fundamentals, including data preparation, model development, validation, evaluation, and interpretation. Strong Python programming skills and familiarity with command-line environments and version control tools such as Git. Experience writing structured, reproducible, and maintainable scientific code, with the ability to understand, test, and explain your implementation. The ability to assess computational predictions critically and relate results to the underlying scientific question. An interest in implementing, testing, and improving computational methods as part of a collaborative research team. Excellent written and spoken English. Experience in one or more of the following areas would be an advantage: Generative protein or binder design, including building models, or adapting or evaluating existing models. Protein complex prediction (co-folding), molecular docking, or modelling protein-protein binding. Antibody biology or modelling antibody-antigen, BCR, or TCR interactions. Running computational workloads on HPC or cloud environments and developing reusable scientific workflows. The position is based in Utrecht and is available for 32–40 hours per week, with approximately 2–3 days per week in the office. The proposed gross annual/monthly base salary range for this position, in the primary location, based on a full time schedule is: EUR59.568,00---89.352,00 The final salary offer will depend on several factors, including your skills, qualifications, and experience. In addition to base salary, this position is eligible for additional forms of compensation, such as discretionary bonuses and long-term incentives. Benefits When you join Genmab, you become a part of a culture that supports your physical, financial, social, and emotional well-being. Our benefits include, but are not limited to: Pension Health insurance and wellness benefits Paid time off Employee support programs Further details on eligibility for compensation and benefits based on the role will be provided during the recruitment process. About Genmab Genmab is an international biotechnology company with a core purpose to improve the lives of patients through innovative and differentiated antibody therapeutics. For 25 years, its hard-working, innovative and collaborative team has invented next-generation antibody technology platforms and harnessed translational, quantitative and data sciences, resulting in a proprietary pipeline including bispecific T-cell engagers, antibody-drug conjugates, next-generation immune checkpoint modulators and effector function-enhanced antibodies. By 2030, Genmab’s vision is to transform the lives of people with cancer and other serious diseases with Knock-Your-Socks-Off (KYSO®) antibody medicines. Established in 1999, Genmab is headquartered in Copenhagen, Denmark with international presence across North America, Europe and Asia Pacific. For more information, please visit Genmab.com and follow us on LinkedIn and X. Genmab is committed to protecting your personal data and privacy. Please see our privacy policy for handling your data in connection with your application on our website Job Applicant Privacy Notice (genmab.com). Please note that if you are applying for a position in the Netherlands, Genmab’s policy for all permanently budgeted hires in NL is initially to offer a fixed-term employment contract for a year, if the employee performs well and if the business conditions do not change, renewal for an indefinite term may be considered after the contract.

Core Responsibilities

Analyze protein structures and binding surfaces, apply and benchmark generative design and protein complex prediction models, and build workflows to filter and prioritize antibody designs for experimental testing. Collaborate with computational and laboratory scientists to interpret validation results, improve design approaches, and deliver reproducible scientific software and clear communication of findings.

Requirements

A PhD in a relevant discipline, with hands-on experience applying machine learning to protein structures or protein-protein interactions, is required; candidates completing their PhD before the anticipated start date may apply. Applicants should have strong Python skills, knowledge of machine learning fundamentals and reproducible software practices, and the ability to critically assess computational predictions and communicate findings in English.

Benefits

• Pension
• Health Insurance
• Wellness Benefits
• Paid Time Off
• Employee Support Programs
• Discretionary Bonuses
• Long-Term Incentives