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Tier-1 employer, metro location, generalist Senior Data Scientist title, and mid-level experience increase applicant competition.
Strong protein and wet-lab domain requirements limit cross-industry transferability.
Explicit 6+ years requirement plus domain-specific MLOps and protein expertise raises screening strictness.
Design, build, and maintain scalable, quality-controlled data pipelines specifically for protein sequence, structure, and function to support predictive model training and reuse across research programs.
Develop and own reusable frameworks for ML model deployment, inference, validation, and monitoring to integrate trained models into ongoing projects reliably.
Facilitate cross-functional collaboration between ML developers, wet-lab scientists, and domain experts to align data, modeling, and experimental requirements and coordinate technical delivery across distributed teams.
Bachelor's degree in Computational Biology, Bioinformatics, Life Sciences, Computational Chemistry, Chemical Engineering, Materials Science, Data Science, or related quantitative field with relevant experience.
Experience Requirement: Bachelor's degree with 6+ years relevant experience, or Master's degree with 4+ years relevant experience, or PhD.
Strong experience building scalable data pipelines using Python and/or SQL; experience with pipeline automation tools like Databricks preferred.
Hands-on experience managing end-to-end MLOps for machine learning models, including deployment and inference workflows.
Experience bridging scientific domains (e.g., computational biology, chemistry, materials science) with data engineering and MLOps to support predictive modeling of protein data.
Proven ability to build reusable, maintainable data and model deployment systems that scale across discovery pipelines and enable reproducibility and data quality control.
Skilled in coordinating cross-disciplinary technical teams and translating complex scientific and engineering needs into reliable production-ready solutions.