Scientific Business Analyst, Scientific AI

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Requirements:

• PhD with 15+ years of industry experience in life sciences with extensive domain knowledge in drug discovery (target ID through lead optimization), preclinical development, CMC (all drug modalities), or product quality testing.
• Proven track record of defining and implementing AI/ML-driven use cases in productized environments.
• Collaborated with cross-functional teams, including product managers, software engineers, and scientific stakeholders.
• Performed extensive exploratory data analysis and workflow optimization to enable scientific outcomes not previously possible
• Engaged diverse audiences, from scientists to executive stakeholders using your excellent communication and storytelling abilities

Responsibilities:

• Investigate customer datasets to identify gaps, enrichment opportunities, and AI-readiness factors.
• Collaborate with customers to define, iterate, and refine AI/ML-driven scientific use cases.
• Interview scientists and guide them in expanding and leveraging their data for AI applications.
• Perform exploratory data analysis (EDA) and define data transformations for AI/ML use cases.
• Develop workflow diagrams, process mappings, ERDs, AS-IS/TO-BE workflows, and ontology definitions.
• Provide feedback on AI/ML models to enhance scientific outcomes and improve product offerings.
• Conduct technical demonstrations, showcase AI applications, and drive adoption.
• Proactively suggest experiments or data strategies that strengthen customer insights and outcomes.