This is a part-time consulting role for 20hr/week. Duration of the project will be approximately 3-4 months. Preference will go to candidates in the Boston area. 

Your impact:

As the AI Adoption Consultant, you will bridge the gap between cutting-edge artificial intelligence and the complex workflows of a modern biotechnology organization. Your mission is to accelerate the integration of AI tools across the enterprise, moving beyond the "hype" to deliver tangible improvements in operational efficiency and scientific output.

You will partner with leaders in Research, Clinical Operations, Finance, IT, and People & Culture to identify high-impact opportunities, design implementation roadmaps, and lead a culture-shifting training program to ensure every employee is "AI-fluent."

Your day-to-day:  

• Conduct deep-dive discovery sessions with functional leads (Finance, IT, Research, etc.) to map existing workflows and identify bottlenecks.
• Build a prioritized "AI Opportunity Roadmap" that balances quick wins with long-term strategic shifts.
• Evaluate and recommend third-party AI tools or internal LLM applications that meet the specific security and compliance standards of the biotech industry.
• Collaborate with the CIO and IT team to ensure AI deployments are scalable, secure, and integrated into the existing tech stack.
• Develop and deliver tailored training modules for diverse audiences—from "AI for Scientists" to "Prompt Engineering for Finance Professionals."
• Create "Office Hours" and internal communities of practice to foster continuous learning and peer-to-peer knowledge sharing.
• Work with Legal and IT to establish clear guidelines for the ethical use of AI, particularly regarding sensitive data and intellectual property.

Must-Haves:

• Experience: 5+ years of experience in digital transformation, management consulting, or a similar role, with at least 1-2 years focused specifically on AI/ML implementation.
• Industry Knowledge: Direct experience in the Biotech or Pharmaceutical sector is highly preferred.
• Technical Fluency: A strong understanding of Large Language Models (LLMs), Generative AI tools, and data orchestration. You don't need to be a coder, but you must be able to speak the language of both data scientists and end-users.
• Communication: Exceptional ability to translate complex technical concepts into "business value" for non-technical stakeholders.
• Education: Bachelor’s degree in a relevant field (CS, Data Science, Business, or Life Sciences).