Data & AI Analyst

Key Responsibilities

• Continuously stay current with emerging data, AI, and analytics innovations, assessing their relevance and responsible application within a regulated clinical research environment.
• AI Use Case Discovery & Requirements (Business-to-Technical): identify high‑value AI opportunities in workflows such as recruitment operations, compliance checks, audit readiness, and administrative processes.
• Gather requirements from stakeholders and translate them into clear problem statements, success metrics, and acceptance criteria.
• Produce lightweight artifacts (use case briefs, workflow maps, risk notes) to guide build/test cycles.
• LLM Workflow Design (Human-in-the-Loop): Design and iterate LLM‑supported workflows for document understanding and knowledge retrieval (e.g., summarization, classification, extraction, Q&A), with strong attention to “human review” steps for clinical appropriateness. 
• Build prompt templates, structured output formats, and validation logic for consistent results. 
• Support AI use cases like LLM review of clinical notes/source documents for compliance and audit readiness (where approved), aligning with the organization’s stated opportunities.
• Model & Output Evaluation (Quality, Reliability, Safety): Create evaluation plans and test sets (representative examples, edge cases, failure modes). 
• Measure performance using agreed metrics (accuracy, precision/recall where applicable, extraction fidelity, reviewer agreement). 
• Track and analyze errors; propose changes to prompts, data inputs, and workflow guardrails.
• Maintain documentation of evaluation results to support internal QA expectations and inspection readiness principles (risk focus, documentation, traceability).
• Assist in identifying “low risk / high volume” opportunities to reduce manual effort, consistent with the organization’s interest in automating tedious workflows.
• Produce AI‑supported insights that help teams prioritize work, understand constraints, and improve action and decision-making across the study lifecycle and patient journey to deliver operational efficiency and quality  (within policy and privacy rules).
• Data Handling, Privacy, and Compliance-by-Design Follow data governance standards for handling sensitive data (PHI/PII).
• Ensure AI workflows meet organizational expectations around HIPAA/GCP/21 CFR Part 11 awareness (working with IT/QA for controls).

• Participate in the AI-powered product development lifecycle and support automation for back‑office and operational processes (e.g., routing, triage, reconciliation support, drafting standardized responses, generating checklists).
• Stakeholder Enablement & Adoption: Create training materials and user guidance for AI tools and support deployment of said solutions across the organization (what it does, what it doesn’t do, review steps, escalation paths)
• Gather user feedback and usage patterns; drive iterative improvements.
• Communicate outcomes clearly to non‑technical teams (what changed, why it matters, how to use it).
• Documentation & Operational Excellence: document prompt versions, workflow logic, evaluation results, and release notes. 
• Support change management and “release discipline” to avoid disruption to site operations.

• Perform other duties as assigned to support organizational goals.

Skills, Knowledge and Expertise

• Strong technical literacy. Proven ability in converting complex use cases into simple solutions, supporting development of custom applications on platforms, and/or building out AI-enabled enterprise solutions
• Demonstrated proficiency in data and AI technologies and software product development; strong understanding of AI-enabled software development lifecycle (SDLC), product steering, product management best practices, LLM workflow design, multi-model solutions, and related commercial applications
• Demonstrated success in supporting enterprise projects and cross-functional initiatives in clinical research and / or healthcare
• Broad understanding in technical subject matters such as IT operations, cloud operations and security, IT engineering and systems administration in clinical research and / or healthcare
• Broad knowledge of HIPAA, 21 CFR Part 11, GCP, data privacy regulations and frameworks in clinical research and / or healthcare
• Strong organizational, time management, problem solving, and project management skills to meet firm deadlines.
• Executive-level written and verbal communication and presentation skills
• Well-developed interpersonal and listening skills and the ability to work well independently and collaboratively within a team environment, building trusted relationships with clients and sponsors, and with all levels within the organization.
• Well-developed strategic, analytical and problem-solving capabilities
• Ability to effectively handle multiple tasks and adapt to changes in workloads and priorities
• Must possess a high degree of professionalism, integrity, dependability, respect of others, self-motivation, and exemplify a strong work ethic.
• Ability to handle highly sensitive information in a confidential and professional manner.
• May be required to travel up to 10% of the time, dependent on business needs.