Senior Machine Learning Scientist

Novo Nordisk; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Job summary

Novo Nordisk in London seeks a Senior Machine Learning Scientist – BioAI to develop biological foundation models and predictive methods for therapeutic discovery. You will partner with experimental teams to translate biological questions into actionable models and deliver reproducible tools for scientists.

This role emphasizes hands-on method development, mentoring colleagues, and contributing to publications and internal knowledge sharing.

Qualifications

• PhD in ML, computational biology, bioinformatics or related field with substantial AI/ML experience.
• Deep learning research on biological data with depth in single-cell omics, biological sequences, or cellular imaging.
• Strong Python and PyTorch skills including model implementation, training, fine-tuning, debugging, and GPU inference.
• Experience with representation/self-supervised learning on biological foundation models.
• Experience building reproducible ML pipelines with version control, tested environments, and data management.
• Strong statistical and model-evaluation skills with proper baselines, validation design, batch effects, confounding, and uncertainty assessment.
• Ability to own research projects and collaborate with experimental/computational colleagues.
• Clear written and verbal communication of methods and implications to diverse audiences.
• Publications or accessible code showing research or software contributions.

Responsibilities

• Develop biological foundation models and predictive methods for target identification and prioritization, disease biology, and mechanism-of-action studies.
• Partner with experimental scientists to define questions and test model predictions.
• Define project objectives, milestones, and success criteria aligning model performance with research decisions.
• Curate and harmonize biological datasets, address metadata quality and reproducible processing.
• Develop reusable workflows for model training and inference and promote adoption by research teams.
• Research model architectures and self-supervised methods for single-cell, omics, and imaging data, including multimodal approaches.
• Train, fine-tune, and debug models in PyTorch on GPU/HPC, balancing performance and cost.
• Explore generative/agentic AI workflows for hypothesis generation, data interpretation, and experiments planning.
• Benchmark against baselines using independent datasets and meaningful endpoints; assess leakage, generalization, uncertainty, and interpretability.
• Communicate findings and limitations to scientists, technical peers, and leadership.
• Mentor colleagues and maintain reproducible experiments and responsible data/model use.
• Contribute to publications, IP, and internal knowledge sharing; evaluate emerging methods for BioAI priorities.

Skills

Deep learning for biology

Python

PyTorch

Research leadership

Communication

Publication and code contributions

Model evaluation

Biological data analysis

Experiment collaboration

Education

PhD in ML / computational biology / bioinformatics or related field

Tools

Python

PyTorch

GPU inference

Job description

Job Title: Senior Machine Learning Scientist – BioAI

Department: BioAI

Location: London, UK

Your impact begins here

The ML Scientist will develop AI methods for biological discovery, focusing on cellular foundation models and complex biological data. You will research model architectures and training approaches, adapt models to priority discovery questions, and work with experimental teams to validate predictions. The role combines hands-on method development with delivering reproducible tools for scientists. You will guide junior colleagues and share research through presentations and publications.

What You’ll Bring

• PhD in relevant degree domain including machine learning, computational biology, bioinformatics, computer science, or a related quantitative or life science discipline, with substantial AI/ML experience.
• Demonstrated deep learning research for biological data, with depth in at least one relevant domain such as single-cell omics, biological sequences, or cellular imaging.
• Strong Python and PyTorch skills, including model implementation, training, fine-tuning, debugging, and GPU inference.
• Practical experience with representation or self-supervised learning, including pretraining, adapting, or systematically evaluating biological foundation models.
• Experience building reproducible analysis or ML pipelines using version control, testing, documented environments, and sound data management.
• Strong statistical and model-evaluation skills, including appropriate baselines, validation design, batch effects, confounding, and uncertainty assessment.
• Ability to own research projects and collaborate with experimental and computational colleagues to translate biological questions into useful models.
• Clear written and verbal communication of methods, limitations, and biological implications to audiences with different expertise.
• Evidence of research or software contributions through publications, accessible code, or examples of delivered scientific tools.

What You’ll Do

• Develop biological foundation models and predictive methods for target identification and prioritization, disease biology, and mechanism-of-action studies.
• Partner with experimental scientists to define biological questions, incorporate biological knowledge into models, and design experiments that test model predictions.
• Define project objectives, milestones, and success criteria that connect model performance to research decisions and measurable scientific impact.
• Work with data and engineering teams to curate and harmonize biological datasets, addressing metadata quality, batch effects, and reproducible processing.
• Develop reusable workflows for model training and inference, support adoption by research teams, and assess improvements in decision quality and research efficiency.
• Research model architectures and self-supervised training methods for single-cell, omics, and imaging data, including multimodal approaches where scientifically justified.
• Train, fine-tune, and debug models in PyTorch on GPU and high-performance computing infrastructure, balancing performance with computational cost.
• Explore generative and agentic AI workflows that support biological hypothesis generation, data interpretation, and experimental planning.
• Benchmark against strong baselines using independent datasets and biologically meaningful endpoints; assess data leakage, generalization, uncertainty, and interpretability.
• Communicate findings, model limitations, and recommendations clearly to experimental scientists, technical colleagues, and leadership.
• Mentor colleagues through scientific guidance and code review, and maintain clear documentation, reproducible experiments, and responsible data and model use.
• Contribute to publications, relevant intellectual property, and internal knowledge sharing, and evaluate emerging methods for their value to BioAI priorities.

Who You’ll Work With

The BioAI team is part of Novo Nordisk’s AI and Digital Innovation (ADI) area. We develop AI methods and biological foundation models to identify therapeutic targets, understand disease mechanisms, and predict cellular responses. We combine single-cell and multi-omics data with imaging and perturbation experiments to generate testable hypotheses. Working with experimental scientists, computational biologists, and engineers across Novo Nordisk and external partners, we translate these insights into better decisions and faster therapeutic discovery.

We value a diverse and inclusive culture where colleagues collaborate, share ideas, and help each other grow. Our work starts and ends with the patient, with a focus on chronic diseases such as diabetes and obesity. We encourage ingenuity and innovation in an enjoyable workplace, supported by competitive compensation and a healthy work-life balance.

What You Can Expect Here

This role is based in our London office, 3 days a week, with travel to Denmark about once or twice a year.

0-10% overnight travel required.

You will be trusted to own your work and supported to perform at your best. Leading some projects and contributing to others gives you room to grow your skills, take on greater responsibility and make your voice heard, in a team that shares what it learns.

Application Support

We are an equal opportunities employer, and we commit to an inclusive recruitment process and equality of opportunity for all our job applicants. If you're a person with a disability, if you're neurodivergent, and need any adjustments to be made during the application and selection process, please send an email to [e-mail]. Please include your name, the role you are interested in and the type of adjustment your need.

We do not need to attach a cover letter, but please include a few sentences in your resume or CV about why you are applying. To keep the process efficient and fair, please do not add a photo to your CV.

We commit to an inclusive recruitment process and equality of opportunity for all our job applicants.

Application deadline: 11 November 2026.