Scientist - Machine Learning AI

Katalyst HealthCares & Life Sciences Inc

  • South Plainfield, NJ
  • 3 days ago

    Highlights

    The role involves curating and harmonizing biological data, building reproducible computational workflows, training and validating predictive models, and integrating protein structural representations with biological data to generate testable scientific hypotheses. The Scientist - Machine Learning AI develops and applies machine learning and statistical methods to analyze protein structures, phenotypic information, and multimodal biological datasets.

    Numbers & Facts

    LocationSouth Plainfield, NJ

    Description

    The Scientist - Machine Learning AI develops and applies machine learning and statistical methods to analyze protein structures, phenotypic information, and multimodal biological datasets. The role involves curating and harmonizing biological data, building reproducible computational workflows, training and validating predictive models, and integrating protein structural representations with biological data to generate testable scientific hypotheses. The scientist collaborates with cross-functional research teams to support data-driven drug discovery and biological research initiatives.

    • Advanced degree (M.S. or Ph.D.) in computational biology, bioinformatics, computational chemistry, biophysics, or a related field
    • Hands-on experience applying machine learning to biological, chemical, or biomedical data
    • Experience working with protein structure data and/or structure-derived features in a research or drug discovery setting
    • Experience integrating or modeling phenotypic and multimodal datasets
    • Strong Python programming skills and experience with scientific computing and machine-learning libraries
    • Ability to independently execute defined project work and deliver high-quality outputs on an agreed timeline
    • 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

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