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Senior scientist applied machine learning (sevilla)

Sevilla
Bristol Myers Squibb
Publicada el 27 mayo
Descripción

Working with Us

Challenging. Meaningful. Life‑changing. Those aren’t words that are usually associated with a job. But working at Bristol Myers Squibb is anything but ordinary. Here, uniquely interesting work happens every day, in every department. From optimizing a production line to the latest breakthroughs in cell therapy, this work transforms the lives of patients and the careers of those who do it. You’ll get the chance to grow and thrive through opportunities uncommon in scale and scope, alongside high‑achieving teams. Take your career farther than you thought possible.

Why this role?

The Predictive Biology & AI team at CITRE (Seville, Spain) is looking for a Scientist / Senior Scientist in Machine Learning to help push the boundaries of how AI can transform biomedical research. This is a role for someone who enjoys both depth and impact developing and training advanced models, exploring new scientific questions, and translating ideas into applications that matter. Depending on your experience, you will either drive or take ownership of key research directions, while contributing to a collaborative and fast‑evolving environment. CITRE is BMS’s European center for computational innovation, where AI is embedded across the drug discovery pipeline.

Key Questions We’re Working On

- How can foundation models be designed to capture the complexity of diseases such as cancer and neurodegeneration?
- How do we combine multimodal data‑omics, imaging (including MRI and other neuroimaging modalities), clinical signals, and text into unified representations of biology?
- How can predictive approaches anticipate patient response and support more precise, data‑driven therapies?

What You Will Do

- Build and apply advanced machine learning models, with a focus on multimodal and foundation model approaches spanning transcriptomics, genomics, imaging (with particular emphasis on MRI and neuroimaging), and biomedical text.
- Train and evaluate modern deep learning architectures (e.g., Transformers, VAEs, ViTs, GNNs), including self‑supervised and generative methods.
- Develop models that support key drug discovery tasks such as target identification, biomarker discovery, and patient stratification.
- Work with large‑scale biological and imaging datasets, including structural and functional MRI.
- Create scalable and reproducible ML pipelines on cloud and/or HPC environments.
- Collaborate closely with computational and experimental scientists; contribute to or lead projects depending on your experience.
- Exchange ideas and best practices with teammates to collectively raise the quality of the work.
- Communicate insights clearly, ensuring your work informs both scientific understanding and strategic decisions.

What You Bring

- Ph.D. in Machine Learning, Bioinformatics, Computer Science, Statistics, Applied Mathematics, Physics, Electrical Engineering, or a related field.
- Strong hands‑on experience with modern machine learning and deep learning methods; exposure to foundation models or multimodal learning is a plus.
- Proficiency in Python and experience with PyTorch and the broader scientific Python ecosystem.
- Experience with cloud platforms (AWS, Azure, GCP) and/or HPC systems (e.g., SLURM).
- Familiarity with Docker, Git, and reproducible research practices.
- Experience working with complex, large‑scale datasets; background in biological, clinical, or imaging data is advantageous.
- Evidence of impactful work (publications, projects, or applied contributions), appropriate to your career stage.
- Ability to work independently while thriving in collaborative, multidisciplinary settings.
- Clear and effective communication skills in English.
- Genuine interest in applying AI to biomedical and healthcare challenges.

Desirable Extras

- Familiarity with MRI and neuroimaging data (e.g., structural MRI, fMRI, diffusion imaging).
- Experience with foundation model training strategies (masked, contrastive, autoregressive) and multimodal architectures.
- Experience with LLMs and related tools (e.g., HuggingFace, PyTorch Lightning, weights & biases).
- Background in neuroscience and/or oncology.
- Previous experience in pharma, biotech, or healthcare research.
- Experience integrating multimodal datasets (omics, imaging, clinical, time‑series).

Equal Opportunity Employer

Bristol‑Myers Squibb is an equal‑opportunity employer. Qualified applicants will receive consideration for employment without regard to gender identity, race, color, religion, sexual orientation, national origin or disability.

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