Principal Machine Learning Research Scientist

About the role

In this role you will advance the methodological capabilities of the Advanced Data Science team within the Data Sciences organization. You will conduct applied machine learning research that goes beyond standard off‑the‑shelf approaches and creates new capabilities for high‑value scientific and decision‑making workflows in pharmaceutical R&D.

Key responsibilities

• Investigate emerging methods such as large language model reasoning, multimodal representation learning, causal machine learning, digital twins, uncertainty quantification, and sequential decision‑making.
• Translate promising research into reference implementations and demonstrable capabilities for priority R&D use cases, working closely with Applied AI Engineers, software engineers and business stakeholders.
• Lead research collaborations with academic institutions and external partners, contributing to publications, conference presentations and other scientific outputs.
• Partner with IT, platform and engineering teams to move validated methods toward broader implementation and production.

Required profile

• PhD in machine learning, AI, computer science, statistics, applied mathematics, biomedical engineering or a related quantitative discipline.
• Minimum 5 years of professional or research experience with a strong track record of peer‑reviewed publications in top conferences (e.g., NeurIPS, ICLR, ICML).
• Deep expertise in one or more areas such as foundation models, multimodal learning, causal ML, digital twins or uncertainty quantification.
• Proven ability to design reproducible computational experiments, establish baselines and conduct rigorous ablation studies.

Required skills

• Python programming
• Machine‑learning frameworks: PyTorch, JAX, TensorFlow
• Large language models and foundation model reasoning
• Multimodal representation learning
• Causal machine learning
• Digital twins and probabilistic modelling
• Uncertainty quantification and sequential decision‑making