7+ years in business analysis, process improvement, or management consulting — ideally in pharma, biotech, life sciences, or another regulated industry.
- Demonstrated experience identifying and delivering AI/automation use cases (generative AI, ML, RPA, or analytics) in a business setting.
- Strong process-mapping and requirements-gathering skills; fluency with tools like Visio/Lucidchart, Jira, and BI platforms.
- Ability to communicate clearly with both non-technical executives and technical/data teams.
- Comfort working independently across multiple departments with ambiguous, evolving problems.
- A pragmatic bias toward measurable outcomes over technology for its own sake
Job Title: Business Analyst, AI Enablement with recent Pharma domain
Location: Remote(EST/CST candidates only)
Visa: USC, GC, EADs
Exp level: 13+ years
Mut have:
- Working knowledge of pharma functions (clinical operations, regulatory, CMC/manufacturing, pharmacovigilance, medical affairs, commercial).
- Familiarity with GxP, data integrity (ALCOA+), 21 CFR Part 11, and AI governance/risk frameworks.
- Hands-on experience with LLM-based tools, prompt design, or low-code automation platforms.
Job description:
What You'll Do
- Discover use cases. Partner with department leads to map current workflows, identify pain points, and pinpoint where AI (LLMs, automation, predictive models, document intelligence) can deliver real value.
- Prioritize ruthlessly. Score opportunities by impact, feasibility, cost, and risk. Build a roadmap that sequences quick wins ahead of long-horizon bets.
- Translate between worlds. Turn business needs into clear requirements for technical teams and vendors; turn technical capabilities into plain-language options for stakeholders.
- Prototype and pilot. Stand up lightweight proofs-of-concept (often with off-the-shelf AI tools) to validate value before larger investment.
- Measure outcomes. Define success metrics, baseline current state, and quantify the impact of deployed solutions.
- De-risk adoption. Work with IT, Quality, and Legal/Compliance to ensure use cases meet data-privacy, GxP, validation, and regulatory expectations appropriate to a clinical-stage pharma environment.
- Drive change. Document new workflows, train end users, and support adoption so solutions actually stick.