Applied ML Scientist, Protein Engineering (Remote)

A-Alpha Bio; United States; Híbrido; USD 139.000 - 175.000; Tempo integral

Resumo da vaga

A-Alpha Bio, a Seattle-based biotechnology platform, is seeking a Scientist II to lead computational analyses on large-scale protein interaction datasets, develop ML models, and collaborate with experimental teams. You will connect sequence and structure data with experimental measurements to derive insights and guide future experiments.

Responsibilities include defining analysis plans, benchmarking models, and delivering reproducible results for internal projects and external collaborators.

Qualificações

• PhD or 10+ years in large-scale data analysis with biology sequence/structure.
• Strong Python for scientific computing, data analysis, ML model development.
• Experience with large experimental datasets and defensible data quality decisions.
• Ability to translate questions into executable, reproducible plans and communicate results clearly.

Responsabilidades

• Define analysis plans for large-scale protein interaction datasets (often 1M PPIs).
• Develop, train, and evaluate ML models using experimental data, sequences, and structures.
• Collaborate with experimental teams to shape study designs and interpret results.
• Deliver presentations and datasets to internal and external partners, with clear documentation.
• Translate project work into reusable analytical tools and maintainable Python code.
• Advance computational approaches to extract value from AlphaSeq data for protein engineering.

Conhecimentos

Python for scientific computing

Data analysis

ML model development

Clear communication

Reproducible analyses

Formação

PhD in a relevant field

Bachelor’s degree +10+ years experience

Ferramentas

Git

PyTorch

TensorFlow

Hugging Face

Descrição da vaga

A-Alpha Bio, a Seattle-based biotechnology platform, is seeking a Scientist II to lead computational analyses on large-scale protein interaction datasets, develop ML models, and collaborate with experimental teams. You will connect sequence and structure data with experimental measurements to derive insights and guide future experiments.

Responsibilities include defining analysis plans, benchmarking models, and delivering reproducible results for internal projects and external collaborators.