Clinical Development Data Science Intern

Genmab; Princeton (NJ); Hybrid; USD 34,000 - 48,000; Full time

Job summary

Genmab is seeking a Clinical Development Data Science Intern in Princeton, NJ. You will join the Clinical Development Data Science team and contribute to AI/ML initiatives across the clinical lifecycle, focusing on transformer-based models, NLP, and generative AI applied to oncology challenges. The role targets Ph.D.

candidates in quantitative fields, with strong Python and ML/NLP experience. The internship runs ~10 weeks (June–August 2027) in a hybrid schedule with three days in the office and

Qualifications

• Pursuing a Ph.D. in a quantitative or computational field; Master’s acceptable.
• Strong Python proficiency for ML/NLP and scientific computing.
• Experience applying ML/NLP to real datasets.
• Ability to design, build, and evaluate models end-to-end.

Responsibilities

• Develop ML/NLP pipelines to extract and summarize information from clinical text.
• Build AI-driven tools to assist trial design and data analysis.
• Prototype advanced models for clinical decision support in oncology.
• Document model evaluation, methods, and results for stakeholders.
• Collaborate across interdisciplinary teams to advance projects.

Skills

Python

ML/NLP

Transformers

PyTorch

HuggingFace

Data processing

Communication

Education

PhD program (quantitative)

Masters considered

Tools

Docker

AWS

Git

Airflow

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!

Job Title

Clinical Development Data Science Intern

Why Genmab

At Genmab, interns don’t just learn about the work - they help move it forward. Our internship program provides meaningful, hands-on experience through real projects that directly support our teams, business priorities, and mission to transform the lives of people with cancer and other serious diseases.

From day one, interns are welcomed as part of the team and given opportunities to contribute, collaborate, and make an impact. Along the way, you’ll learn from talented colleagues and senior leaders, build new skills, expand your professional network, and gain exposure to the innovative work happening across Genmab.

We’re always looking for smart, curious, purpose-led people who are ready to bring fresh perspectives and bold ideas. Explore our website to learn more about Genmab and how you could play a role in our bold, extra[not]ordinary® future.

Job Overview

The Clinical Development Data Science Intern will join Genmab’s Clinical Development Data Science team and contribute to innovative AI/ML initiatives across the clinical development lifecycle. This role is ideal for a highly technical, impact-driven data scientist who is pursuing a Ph.D. in a quantitative or computational discipline, and who is excited to apply modern machine learning, natural language processing, and generative AI to real clinical and scientific challenges in oncology.

The intern will support projects such as building AI-powered tools to assist trial design, developing analytics pipelines for clinical and safety data, and prototyping advanced models that help clinicians and scientists make better, faster decisions.

What you’ll do

• Develop machine learning and NLP pipelines to extract, organize, and summarize information from clinical trial protocols, regulatory documents, and unstructured biomedical text.
• Support the development of AI-driven tools that:
  • Accelerate protocol design through information retrieval, extraction, and summarization.
  • Enhance pharmacovigilance and safety monitoring through automated literature surveillance, risk modeling, anamoly detection, and severity prediction
  • Enable portfolio planning and decision-making through predictive modeling of key milestones and drivers
  • Support regulatory intelligence and affairs through analysis and synthesis of documents, precedents, and health authority decisions.
• Apply advanced modeling techniques including transformers, generative models, survival analysis, clustering, and other ML approaches.
• Experiment with explainability methods (e.g., SHAP, LIME) to improve the interpretability of models used in clinical decision-support contexts.
• Contribute to documentation, model evaluation, technical reviews, and final presentations to internal stakeholders.

Required qualifications, capabilities and skills

• Sustained progress in a Ph.D. program in a quantitative or computational discipline (Computer Science, Data Science, Statistics, Physics, Computational Chemistry, Computational Biology, Applied Mathematics, Engineering, or related quantitative/technical field).
  • Master’s students with strong applied experience may also be considered.
• Strong proficiency in modern Python for machine learning and scientific computing (e.g., PyTorch/TensorFlow, scikit-learn, spaCy, Hugging Face transformers, pandas, NumPy).
  • Candidates should be experienced in building modular scripts and reusable modules, command-line utilities, and structured codebases for data processing and machine learning workflows.
• Experience applying ML or NLP methods to real-world datasets.
• Ability to design, build, and evaluate computational models end-to-end.
• Excellent analytical problem-solving abilities and intellectual curiosity.
• Clear written and verbal communication skills.
• High attention to detail, scientific rigor, and commitment to producing high-quality work.
• Ability to work independently as well as collaboratively within interdisciplinary teams.

Preferred qualifications, capabilities and skills

• Experience with large language models (LLM), retrieval-augmented generation (RAG), or generative AI workflows.
• Prior work involving unstructured document parsing (e.g., PDFs, scanned text, scientific literature).
• Full-stack data science skill set, including experience with:
  • Writing production-quality, modular, and reusable Python code
  • Building scalable, reproducible systems and packaging code for broader use
  • Containerization (e.g., Docker) and reproducible compute environments
  • Workflow or pipeline orchestration tools (Airflow, Prefect, Dagster, etc.)
  • Cloud computing environments (AWS, Azure, GCP)
• Experience using Git repositories and version-control workflows, including:
  • Branching strategies, pull requests, peer code reviews
  • Good commit hygiene and collaborative development practices
  • Familiarity with GitHub, GitLab, or similar platforms
• Exposure to CI/CD, structured logging, automated testing, or other professional engineering practices.
• Experience with MLOps concepts such as model packaging, monitoring, reproducibility, and deployment is a strong plus.

General Intern Information – Date/Location/Schedule

Internships will take place over 10 weeks from June – August 2027. This role will be based in Princeton, NJ and operate on a required hybrid schedule – 3 days in the office and 2 days remote per week. This role is not eligible for work visa sponsorship at the time of the application or in the future.

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.

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