Principal Machine Learning Scientist

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Company Overview

A leading global healthcare and life sciences organization is advancing drug discovery and development through artificial intelligence, data science, and computational technologies. Its research organizations are leveraging novel computational models, large-scale datasets, and AI-driven approaches to accelerate research and develop innovative medicines for patients worldwide.

The organization has established a unified Computational Sciences Center of Excellence focused on harnessing data and Artificial Intelligence to support scientists across research and early development teams. The group promotes collaboration, data sharing, and access to advanced computational models to enable transformative scientific discoveries.

The Opportunity

The organization is seeking a highly motivated and collaborative Senior/Principal Machine Learning Scientist to join its Perturbation Biology group within the Department of AI for Biology & Translation (AIBT).

The successful candidate will develop next-generation machine learning models that derive actionable insights from large-scale, high-content perturbation experiments for target and drug discovery. The role requires a strong understanding of machine learning applied to sequencing-based perturbation data, a passion for interdisciplinary research, and a commitment to using advanced technology to improve healthcare outcomes.

The successful candidate will be expected to develop and implement innovative research ideas, lead high-profile projects in collaboration with therapeutic area teams, and contribute research to leading machine learning and scientific publications and venues.

Key Responsibilities

• Design and apply predictive machine learning algorithms for lab-in-the-loop perturbation screens supporting drug and target identification.
• Develop machine learning approaches for sequencing-based perturbation datasets and other high-content experimental data.
• Work with and integrate multiple data modalities, including molecular structures, omics data, images, and text.
• Collaborate with interdisciplinary teams consisting of biologists, chemists, data scientists, and other stakeholders.
• Build and scale machine learning techniques to process massive datasets.
• Support the deployment and implementation of novel machine learning algorithms.
• Lead high-profile research projects in collaboration with therapeutic area teams.
• Publish research in top-tier machine learning venues and scientific journals.
• Present research findings at internal and external scientific venues, conferences, and workshops.
• Contribute to interdisciplinary research initiatives that advance AI applications in biology and drug discovery.

Required Qualifications

• PhD in a quantitative discipline such as Computer Science, Statistics, or Mathematics, or a PhD in a physical or life science such as Chemistry or Biology with a strong quantitative focus.
• For Senior Machine Learning Scientist: 0–2 years of experience after completing a PhD.
• For Principal Machine Learning Scientist: 2–7 years of experience after completing a PhD.
• Proven track record of developing and applying advanced machine learning models in research or industry environments.
• Demonstrated interest in applying computational and machine learning approaches to biology and chemistry for the discovery and development of disease treatments.
• Proficiency in scientific programming using Python.
• Extensive experience with machine learning frameworks and libraries such as PyTorch, JAX, or TensorFlow.
• Strong background in statistics, probabilistic modeling, and data analysis.
• Excellent communication, collaboration, and problem-solving skills.
• Strong publication record and experience contributing to research communities.
• Experience publishing or presenting research at leading venues such as NeurIPS, ICML, ICLR, CVPR, or ICCV.

Preferred Qualifications

• Practical experience with predictive modeling of perturbation datasets to support experimental design.
• Experience with predictive or generative modeling involving molecules and chemistry applications.
• Experience integrating multimodal datasets, particularly multiple measurement modalities and/or clinical patient data.
• Experience working across interdisciplinary scientific and technical teams.
• Demonstrated ability to translate machine learning research into practical applications in biology, drug discovery, or related scientific fields.

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