Asks for

The ad says“Design active learning, uncertainty-aware, or Bayesian optimization strategies to prioritize experiments and guide data generation across modalities”
The ad says“Experience working with large-scale datasets and in high-performance computing environments, including cloud-based platforms (e.g., AWS)”
The ad says“Design active learning, uncertainty-aware, or Bayesian optimization strategies to prioritize experiments and guide data generation across modalities”
The ad says“Familiarity with reproducible machine learning and software engineering practices, including version control (git), testing, containerization (Docker), workflow orchestration, data versioning, or experiment tracking.”
The ad says“Train, fine-tune, and evaluate foundation models for sequence/structure-to-property prediction and biologics design”
The ad says“Familiarity with reproducible machine learning and software engineering practices, including version control (git), testing, containerization (Docker), workflow orchestration, data versioning, or experiment tracking.”
The ad says“Develop and apply predictive machine learning models for biologics properties, including developability and clinical immunogenicity-related endpoints”
The ad says“Experience with Python scientific computing tools, such as numpy, scipy, pandas, etc.”
The ad says“Experience with Python scientific computing tools, such as numpy, scipy, pandas, etc.”
The ad says“Develop and apply predictive machine learning models for biologics properties, including developability and clinical immunogenicity-related endpoints”
The ad says“Strong proficiency in Python and experience with modern deep learning frameworks such as PyTorch”
The ad says“Strong proficiency in Python and experience with modern deep learning frameworks such as PyTorch”
The ad says“Experience with Python scientific computing tools, such as numpy, scipy, pandas, etc.”

Career Category

Clinical

Job Description

What you will do

Let's do this. Let's change the world. We are seeking a Senior Data Scientist with strong expertise in machine learning and predictive modeling to join the AI & Data for Engineered Biologics team within Amgen's Large Molecule Discovery (LMD) organization. In this vital role you will enable data-driven design and optimization of biologics by developing models that predict key developability and therapeutic properties. You will work with sequence, structure, and experimental datasets to generate actionable insights and guide discovery decisions.

The successful candidate will work in a highly collaborative, multidisciplinary environment, partnering with different experimental teams to build predictive models, train and evaluate protein foundation models, and design active-learning strategies that improve data generation and candidate optimization.

Key Responsibilities

Develop and apply predictive machine learning models for biologics properties, including developability and clinical immunogenicity-related endpoints
Train, fine-tune, and evaluate foundation models for sequence/structure-to-property prediction and biologics design
Integrate multimodal data, including protein sequence, structure, imaging, assay, and experimental metadata, to improve predictive performance and model robustness
Design active learning, uncertainty-aware, or Bayesian optimization strategies to prioritize experiments and guide data generation across modalities
Build reproducible modeling workflows and evaluation frameworks that support rigorous model comparison, validation, and deployment-readiness
Collaborate cross-functionally with scientists and software engineers to translate machine learning innovation into actionable insights for biologics discovery and optimization
Communicate scientific findings and modeling strategies through presentations, technical documentation, and cross-functional forums

What we expect of you

We are all different, yet we all use our unique contributions to serve patients. The collaborative professional we seek is a Senior Data Scientist with these qualifications.

Basic Qualifications

Doctorate degree with 4+yrs in Data Science, Computer Science, Computational Biology, Bioinformatics, Computational Chemistry, or a related field

Master's degree and 8+ years of directly related experience

Preferred Qualifications

Experience developing machine learning models for biological, protein engineering, drug discovery or immunology applications
Strong proficiency in Python and experience with modern deep learning frameworks such as PyTorch
Experience with Python scientific computing tools, such as numpy, scipy, pandas, etc.
Experience with foundation models, representation learning, sequence-to-property modeling, and multimodal learning
Experience integrating sequence, structural, assay, imaging, and metadata sources into reliable, model-ready datasets
Proven ability to apply active learning, Bayesian optimization, uncertainty quantification and model interpretation to scientific problems
Experience working with large-scale datasets and in high-performance computing environments, including cloud-based platforms (e.g., AWS)
Familiarity with reproducible machine learning and software engineering practices, including version control (git), testing, containerization (Docker), workflow orchestration, data versioning, or experiment tracking.
Strong scientific communication skills, with publications in leading ML, computational biology, or protein science venues such as NeurIPS, ICML, ICLR, ISMB, Nature Biotechnology, Nature Methods, Cell Systems, MABS, PNAS or comparable conferences and journals; candidates should highlight representative publications on their resume.