AI Scientist – AI-Driven Target Identification

Job description

Band Level 3 Job Description Summary Location: Cambridge or Basel Onsite Relocation is offered for this role. #LI-Onsite The Oncology Data Science team in Biomedical Research at Novartis works at the intersection of oncology drug discovery, computational biology, AI/ML, and data engineering. We are seeking an enthusiastic AI/ML scientist with strong curiosity for AI-driven drug discovery to join the AI & Innovation team. This role will apply advanced AI approaches to generate insights from complex multi-modal datasets and advance our target and biomarker discovery efforts. Job Description Key responsibilities: Design, develop, and apply advanced AI/ML approaches to extract actionable insights from pre-clinical, clinical and real-world evidence datasets. Demonstrate value of innovative AI techniques in the context of drug target identification, biomolecular interaction modeling, drug development and biomarker discovery. Work with foundational models, including pre-trained, self-supervised, multi-purpose, and multi-modal models to advance generative AI applications in drug discovery. Collaborate with cross-functional teams to develop and adopt best practices for ML-ready data. Contribute to scientific publications and present results at internal and external scientific conferences. Requirements: Ph.D. in Machine Learning, Computer Science, Applied Mathematics, Computational Biology or related field. Strong experience in one or more of the following areas: generative AI, biomedical foundation models, geometric deep learning, multi-modal learning, and large-scale knowledge graphs. Excellent programming skills and proficiency in deep learning frameworks such as PyTorch, with openness to learning new tools and technologies. Practical experience across ML and LLM software stack, including feature engineering, model development, deployment, and validation. Prior experience working with omics data and familiarity with oncology drug development. Excellent communication skills, with the ability to communicate complex data insights and recommendations to cross-functional teams and stakeholders. Demonstrated strong research skills, evidenced by publications in top-tier machine learning or artificial intelligence conferences and/or leading scientific journals. Rewards At Novartis, we’re committed to reimagining medicine together - and rewarding the people who make it happen. The rewards of being part of our team go far beyond base pay and incentives. We also offer a variety of competitive benefits in kind to help you thrive personally and professionally, such as insurance plans, retirement plans, wellbeing resources and global recognition programs. In addition, we provide flexible and hybrid working options, where possible, and a minimum of 14 weeks paid parental leave. Expected Annual Base Salary Range for role: US: 103,600.00 - 192,400.00 USD Annual The salary offered is determined based on gender-neutral objectives, such as relevant skills, competencies and experience in accordance with the Novartis pay setting policy and upon joining Novartis will be reviewed periodically. In addition to your base salary, you may be eligible for a performance-based bonus depending on certain performance parameters. Further details will be provided during the application process. Pay equity is a fundamental principle of our employment policy and reflects our commitment to create a diverse, equitable and inclusive environment that treats all employees with dignity and respect, as outlined in our Code of Ethics. Read our brochure to learn more about our global total rewards offering: https://www.novartis.com/sites/novartis_com/files/novartis-life-handbook.pdf Note: Benefits and compensation may vary by country and are subject to local legal requirements, including provisions of collective bargaining agreements where applicable. A full overview of your compensation package, including any relevant collective bargaining agreement details applicable to your role based on your employment location and Novartis employer entity, will be communicated separately to you during the application process. Why Novartis: Helping people with disease and their families takes more than innovative science. It takes a community of smart, passionate people like you. Collaborating, supporting and inspiring each other. Combining to achieve breakthroughs that change patients’ lives. Ready to create a brighter future together? https://www.novartis.com/about/strategy/people-and-culture 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 EEO Statement: The Novartis Group of Companies are Equal Opportunity Employers. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status. We strive to create an inclusive workplace that cultivates bold innovation through collaboration and empowers our people to unleash their full potential. Accessibility and reasonable accommodations The Novartis Group of Companies are committed to working with and providing reasonable accommodation to individuals with disabilities. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the application process, or in order to perform the essential functions of a position, please send an e-mail to tas.nacomms@novartis.com call +1 (877)395-2339 and let us know the nature of your request and your contact information. Please include the job requisition number in your message. https://www.novartis.com/careers/careers-research/notice-all-applicants-us-job-openings Salary Range $103,600.00 - $192,400.00 Skills Desired Biostatistics, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Graph Algorithms, Learning Agility, Machine Learning (ML), Machine Learning Algorithms, Python (Programming Language), Statistical Analysis, Time Series Analysis