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, 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
Requirements
PhD with 15+ years of industry experience in life sciences with extensive domain knowledge in DMPK / Metabolite ID including ADME (Absorption, Distribution, Metabolism, Excretion), PK/PD Modeling (NONMEM, Phoenix WinNonlin), In vitro / In vivo Studies (microsomes, hepatocytes, animal models), Bioanalytical LC-MS/MS, and CYP450 Enzyme Profiling & Metabolite Identification
Proven track record of defining and implementing AI/ML-driven use cases in productized environments to support DMPK and Metabolite ID efforts
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 excellent communication and storytelling abilities
Advised scientists in a consulting capacity to further research, development, and quality testing outcomes