Remote ML Scientist for Protein Structure & Multimodal Data

Apr; United States; Remote; USD 95,000 - 135,000; Full time

Job summary

Apr is seeking a Scientist, Machine Learning AI for a remote, 12-month contract to develop and apply machine-learning approaches to protein structure and multimodal biological datasets. You will curate data, build reproducible workflows for feature generation, model training, validation, and performance evaluation.

You will integrate protein structural representations with phenotypic data to generate testable hypotheses and prioritize follow-up analyses, and you will write maintainable code and

Qualifications

• PhD or MS in computational biology or related field
• Experience applying machine learning to biological or biomedical data
• Experience with protein structure data or structure-derived features

Responsibilities

• Develop and apply machine-learning and statistical approaches to protein structure, phenotypic, and other multimodal biological datasets
• Curate, harmonize, and quality-control structured and unstructured data from internal and external sources
• Build reproducible computational workflows for feature generation, model training, validation, and performance evaluation
• Integrate protein structural representations with phenotypic and other biological data to generate testable hypotheses and prioritize follow-up analyses
• Implement clear, maintainable analysis code and contribute to shared repositories and technical documentation
• Communicate methods, findings, and recommendations clearly to technical and non-technical stakeholders

Skills

Python programming

ML for biology

Protein structure data

Multimodal data

Education

MS or PhD in computational biology

Tools

SciPy

NumPy

scikit-learn

Python

Job description

Apr is seeking a Scientist, Machine Learning AI for a remote, 12-month contract to develop and apply machine-learning approaches to protein structure and multimodal biological datasets. You will curate data, build reproducible workflows for feature generation, model training, validation, and performance evaluation.

You will integrate protein structural representations with phenotypic data to generate testable hypotheses and prioritize follow-up analyses, and you will write maintainable code and