Biomedical AI Methods Expert

Job Description Summary

Job Description

Responsibilities:

• Work with a team of AI researchers, data scientists and SMEs with core domain expertise to develop and deliver focused, robust, performant AI algorithms and solutions to accelerate drug discovery.

• Stay informed about the latest AI methods, increase understanding of the problem domain, and ask detailed questions to identify potential areas for innovation in AI across the drug discovery pipeline.

• Promote awareness of advancements in AI methods for addressing key research questions within biomedical research.

• Fluently adopt Engineering and Product Development resources to ensure the adoption of AI solutions.

• Help position AI-aided drug discovery contributions to deliver and support progress of BR's portfolio, enable new kinds of therapeutic discoveries, shorten cycle times, and increase efficiency.

Collaboration & partnership

• A respectful, team-player attitude is an absolute must.

• Regularly communicate, engage, align with AICS teams, broader data science community, and senior scientists.

• Initiate and lead key high-value internal collaborations across BR.

• Help design translatable metrics for AI models that will lead to tangible impact in collaboration with BR DAs and FAs.

• Collaborate cross-functionally to translate model outputs into actionable hypotheses and guide experimental design.

What you'll bring to the role:

• 4+ years of significant experience in innovation, development, deployment and continuous support of Machine Learning and modeling.

• Strong hands-on coding proficiency in Python and deep learning frameworks and an expertise in bringing advanced analytics insights and actions to a large research organization

• Wide exposure to representation learning, deep generative modeling, probabilistic reasoning, and explainability approaches. with a deep passion for understanding and explaining the technical concepts underlying ML approaches.

• Strong understanding and experience in using version control systems for developing software (e.g. GitHub, git, subversion, bitbucket, etc.) with expertise in large-scale model training, distributed computation, and foundation model adaptation.

• Publications, patents, or open-source contributions that demonstrate machine learning innovation and expertise, with passion for understanding emerging technologies and how they can integrate into business solutions

• Ability to balance requirements, manage expectations, and drive effective results using a proactive can-do attitude towards identifying and resolving issues, as well as a proven ability to work exceptionally well within complex matrixed teams.

• A deep curiosity and passion for biomedical sciences driven therapeutic discovery.

Nice to have:

• Experience with applying ML to one or more functional areas of core drug discovery like target identification, computational chemistry, protein structure modelling and design or translational medicine is a plus.

Commitment to Diversity & Inclusion:

We are committed to building an outstanding, inclusive work environment and diverse teams representative of the patients and communities we serve.

Benefits and rewards:

Read our handbook to learn about all the ways we'll help you thrive personally and professionally:

https://www.novartis.com/careers/benefits-rewards

For Basel, Switzerland:
Switzerland Accessibility and accommodation

Novartis is committed to working with and providing reasonable accommodation to all individuals. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the recruitment process, or in order to receive more detailed information about the essential functions of a position, please send an e-mail to diversity.inclusion_ch@novartis.com and let us know the nature of your request and your contact information. Please include the job requisition number in your message.

Relevant areas of desired expertise:

Transformer architectures (Text, Vision, DNA/amino-acid sequences, Gene expression vectors);Long-context transformersandefficient attention mechanisms; diffusion models;flow matching;energy-based models; multimodal architectures; graph neural networks; geometric deep learning; Bayesian optimization; Reinforcement learning; Active learning; Variational Inference; Self-supervised learning; Contrastive learning; Domain adaption; Test-time adaptation

Skills Desired