AI Scientific Business Analyst

Aroha Technologies, Inc is seeking an experienced AI Scientific Business Analyst to bridge Life Sciences, Clinical Development, and Artificial Intelligence. The role transforms scientific and business challenges into structured AI requirements for drug discovery, clinical research, and pharmaceutical R&D, while coordinating stakeholders, requirements, and User Acceptance Testing through production release.

Responsibilities:

• Collaborate with Product Managers, Business Leads, AI Engineers, and Data Teams to identify and define AI use cases
• Gather, analyze, and document business, scientific, and functional requirements for AI-driven applications
• Translate clinical and scientific business problems into AI-ready user stories, acceptance criteria, and functional specifications
• Define AI input/output requirements, model expectations, workflows, dashboards, and business success metrics
• Create and maintain:

• User Stories
• Acceptance Criteria
• User Requirements Specifications (URS)
• Functional Requirements Specifications (FRS)
• Process Flows
• Data Flow Diagrams
• Source-to-Target Mapping

• Partner with Data and AI teams to define datasets, model inputs, outputs, and validation requirements
• Lead requirements refinement sessions and stakeholder workshops
• Plan, coordinate, and lead User Acceptance Testing (UAT), including defect management and release readiness
• Monitor project risks, dependencies, assumptions, and communicate project status to stakeholders
• Serve as the primary liaison between scientific business teams and AI engineering teams

Requirements:

• Bachelor's or Master's Degree
• 10+ years of Business Analysis experience
• 8+ years within Pharmaceutical or Life Sciences R&D organizations
• Strong experience supporting: Clinical Research, Clinical Development, Translational Science, Drug Discovery, Oncology (Preferred)
• Experience delivering AI or data-driven business solutions
• Experience working with Product Managers and cross-functional technical teams
• Experience leading User Acceptance Testing (UAT)
• Life Sciences Domain Expertise
• Clinical Development Lifecycle
• Pharmaceutical R&D
• Business Analysis
• Requirements Gathering
• User Stories
• Functional Requirements Specifications (FRS)
• User Requirements Specifications (URS)
• Process Mapping
• AI Use Case Definition
• Requirement Prioritization
• KPI Definition
• Stakeholder Management
• Agile/Scrum Methodologies
• Jira or Azure DevOps
• Excellent Communication and Documentation Skills
• Generative AI
• AI/ML Fundamentals
• Prompt Engineering
• Large Language Models (LLMs)
• AI Agents
• Intelligent Automation
• GxP / Computer System Validation (CSV)
• SQL
• Python
• Healthcare Analytics
• Cloud AI Platforms