Our client, a leading Life Sciences company, is hiring a Scientific Business Analyst, Scientific AI.

Work Location: Middlesex County, Massachusetts

Summary:

The Scientific Business Analyst, Scientific AI will be a critical team member in a unique partnership to industrialize Scientific AI. This role involves direct engagement with customers onsite up to 4-5 days per week in the Boston Region, focusing on customer data exploration, scientific use case development, stakeholder engagement, data analysis and enrichment, workflow documentation, AI model evaluation, customer enablement, and providing strategic insight to amplify scientific discovery and development.

Responsibilities:

• Engage directly with customers onsite up to 4-5 days per week in the Boston Region.
• Customer Data Exploration: Investigate diverse customer datasets, identifying enrichment and AI-readiness opportunities.
• Scientific Use Case Development: Collaborate with customers to define, iterate, and implement innovative scientific AI/ML use cases.
• Stakeholder Engagement: Conduct onsite interviews and workshops to deeply understand customer challenges and data landscapes.
• Data Analysis and Enrichment: Perform exploratory data analysis and define transformation workflows that enable scientific AI.
• Workflow Documentation: Develop visual documentation including workflow diagrams, ERDs, and ontology definitions.
• AI Model Evaluation: Provide practical scientific input on model output, with suggestions to improve real-world performance.
• Customer Enablement: Deliver onsite demonstrations, conduct working sessions, and act as a trusted advisor in AI adoption.
• Strategic Insight: Propose new directions, experiments, or platforms that can amplify scientific discovery and development.

Qualifications:

• PhD with 15+ years of industry experience in life sciences, preferably across pharma, biotech, or health tech, with deep domain expertise in discovery, preclinical, CMC, and/or Quality.
• Extensive hands-on experience or direct oversight in one or more of the following areas: high throughput screening, preclinical toxicology, materials engineering, analytical development, drug substance (DS) synthesis and manufacturing.
• Delivered requirements for AI/ML-driven solutions in operational or productized environments that improved efficiency, reduced cost, and enhanced data utilization.
• Extensive hands-on experience with scientific data workflows and lab automation; exposure to FAIR principles and modern data architecture is a plus.
• Strong coding or scripting background (e.g., Python, Nextflow, AWS, SDKs) and familiarity with scientific tools, databases, and ontologies is preferred.
• Exceptional communication and storytelling ability to engage technical and executive stakeholders.
• Prior experience in customer-facing, consulting, or commercial-scientific interface roles.

Published Category: Technology & Data & AI Solutions