Remote Business Analyst - AI & ENTERPRISE DATA PRODUCTS

Title: Remote Business Analyst - AI & ENTERPRISE DATA PRODUCTS 
Duration: 6 months/contract
Must have pharmaceutical, biotech, medical device client exp
Must have AI/ML exp

POSITION SUMMARY
We are seeking a Business Analyst to define requirements for an AI-driven application serving internal business functions across Quality / Manufacturing / Clinical / Regulatory / Commercial. 
The application sits on top of a substantial data layer, drawing from multiple enterprise source systems, and uses machine learning to surface insights / generate draft content / classify records / recommend actions for internal users. 
his role is a hybrid of classic business analysis and data product ownership. You will spend as much time on data lineage, source-system semantics, and model evaluation criteria as you will on user workflows. 
Success requires someone comfortable writing requirements for systems that produce probabilistic rather than deterministic outputs, and who understands that in a regulated environment, an AI recommendation is only as defensible as the data provenance and audit trail behind it. 
KEY REPONSIBILITIE 
* AI Solution Requirements 
* Data Layer & Integration Requirements 
* Compliance & Validation 
* Stakeholder Management & Adoption 
QUALIFICATIONS 
REQUIRED
• 4–7 years of business analysis experience, including at least one delivery involving 
   analytics, machine learning, or data-intensive applications. 
• Working SQL proficiency. 
• Demonstrated ability to write requirements and acceptance criteria for systems with non-
   deterministic outputs. 
• Practical understanding of data modeling, source-to-target mapping, and integration 
  patterns. 
• Foundational understanding of machine learning concepts: 
• Excellent written communication; 
PREFERRED
• Experience in pharmaceutical, biotech, medical device, or another FDA-regulated industry. 
• Experience validating or supporting a GxP-relevant AI/ML system, or familiarity with GAMP 
  5 Second Edition guidance on AI/ML. 
• Familiarity with predictive ML patterns where applicable: classification and regression, 
  feature definition, class imbalance, and threshold tuning against business cost of false 
  positives vs. false negatives.