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Strong employer brand and metro location increase applicant density, but niche senior ML governance specialization reduces competition.
ML governance skills transfer across industries, but biotechnology research background and scientific domain expertise are preferred.
Explicit degree alternatives with 8–10+ years experience and specific MLOps/governance requirements make filtering strict.
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Establish and implement standards, governance processes, and operational practices to scale AI/ML capabilities within Large Molecule Discovery.
Ensure reproducibility, security, compliance, and alignment with enterprise requirements for AI/ML assets including code repositories, documentation, and deployment.
Serve as a liaison between scientific teams and technology partners to maintain, audit, and scale AI-enabled scientific solutions and workflows.
PhD degree, or Master’s degree with 8+ years related experience, or Bachelor’s degree with 10+ years related experience.
Experience with machine learning platforms, MLOps, or AI governance programs.
Familiarity with machine learning lifecycle management, model deployment, documentation, validation, and reproducibility standards.
Work Experience Required: Minimum 8 years if Master's degree holder, 10 years if Bachelor's degree holder, PhD holders - experience not explicitly quantified.
Demonstrated experience supporting or managing machine learning operations, governance, or software lifecycle management in complex scientific or research environments.
Ability to translate scientific innovation requirements into operational and governance standards that ensure sustainable AI adoption.
Experience collaborating with cross-functional teams including AI/ML scientists, software engineers, and enterprise technology partners in biotechnology or life sciences domains.