Director and Group Head, Applied AI

Novartis • Cambridge, MA

2d • $194,600 - $361,400 • Hybrid

About The Position

Novartis has embraced a bold strategy to drive a company-wide digital transformation. Our objective is to position Novartis as an industry leader by proactively adopting digital technologies that foster innovative approaches to hasten drug discovery and development. By utilizing both internal and external R&D data with the power of data science, predictive models, generative AI, and machine learning, our objective is to identify new targets, create more effective therapeutic molecules, better predict drug pharmacokinetics and safety risks, refine clinical trial design, and significantly shorten development cycles. The AI4R team leads BR in exploring and applying advanced AI and ML methodologies to generate novel drug discovery insights, and to speed and improve drug discovery efficiency whilst focusing on patients’ needs. AI4R partners with drug discovery teams, raises the level of AI expertise across Biomedical Research (BR) and ensures that BR science keeps up with the rapidly evolving ecosystem of AI technologies by connecting with AI leaders in academia and industry. This leadership role for the Applied AI group of AI4R will be tasked with strong technical, team and project leadership, aligning with biomedical subject matter experts to deeply understand ML opportunities in drug discovery, assessment of the model landscape, leading model benchmarking, and applying the right AI approaches, algorithms, models and workflows to maximize impact on key domains areas of biomedical research that will potentially lead to developing better drugs, faster. Step into a pivotal leadership role at the forefront of scientific innovation, where cutting-edge artificial intelligence meets biomedical research. As Director & Group Head (Applied AI), you will shape how advanced machine learning approaches accelerate drug discovery, translating complex data into meaningful scientific insights that can transform patient outcomes. You will lead high-impact collaborations, guide strategic AI direction across research domains, and empower teams to push the boundaries of what is possible in developing better medicines, faster.

Requirements

• Demonstrated experience in leading core machine learning capability development initiatives across drug discovery teams and use cases.
• Proven experience with foundation model benchmarking in drug discovery applications.
• Hands-on experience applying machine learning to core drug discovery areas such as target identification or computational chemistry.
• Strong experience in large-scale model training, distributed computation, model adaptation, and deployment within machine learning operations frameworks.
• Deep curiosity and passion for biomedical sciences and therapeutic discovery, with ability to explain complex technical concepts clearly.
• Minimum of 12+ years of experience in innovation, development, deployment, and continuous support of machine learning and modeling solutions.
• Strong coding proficiency in Python and deep learning frameworks, with experience using version control systems such as Git.
• Ability to manage complexity, balance priorities, and drive outcomes effectively within matrixed environments using a proactive mindset.

Nice To Haves

• Publications, patents, or open-source contributions demonstrating machine learning innovation and domain expertise.
• Strong curiosity for emerging technologies with pragmatic ability to apply them to real-world business challenges.

Responsibilities

• Define and lead the applied artificial intelligence strategy and multi-year roadmap across drug discovery research.
• Align priorities with portfolio needs, scientific opportunities, and measurable business and research impact.
• Lead multidisciplinary teams to identify, prototype, benchmark, and deploy fit-for-purpose artificial intelligence solutions.
• Govern an applied artificial intelligence portfolio with clear intake, prioritization, resourcing, delivery oversight, and success metrics.
• Establish best practices for problem framing, data readiness, benchmarking, evaluation design, and reproducible model development.
• Drive benchmarking of foundation and task-specific models, enabling transparent trade-offs and informed adoption decisions.
• Partner with engineering teams to scale solutions and embed them into day-to-day scientific decision making.
• Define rigorous evaluation metrics linking model performance to downstream decisions and experimental outcomes.
• Build a culture of scientific rigor, rapid iteration, mentorship, and practical impact across teams.
• Forge strategic academic and industry collaborations to accelerate innovation, benchmarking, and technology transfer.

Benefits

• health, life, and disability coverage
• a 401(k) plan with company contribution and matching
• a range of additional benefits
• a generous time-off package, including vacation, personal days, holidays, and other leave options

What This Job Offers

• Job Type: Full-time
• Career Level: Director
• Education Level: No Education Listed