Scientific Business Analyst – AI/LLM Applications in Drug Discovery

Job Title: Business Analyst - LLM-based Applications in Drug Discovery

Location: 100% Onsite – Cambridge, MA, Lawrenceville, NJ, San Diego, CA or Seattle, WA

Rate type; W2 only

Top Skills:
• Expert in understanding scientific domain context required for LLM-based applications
• Adept in supporting LLM-based and non-LLM-based systems
• Experience training internal and external end users
• Writing test scripts as well as SOP and work instructions
• Life Science experience preferred

Overview

The Business Analyst is a service-oriented, positive, forward-thinking team member, who is responsible for supporting LLM-based solutions to meet business-related requirements within client and the drug discovery process. The Business Analyst collaborates with subject matter experts including scientists across Research to understand current/future business processes and requirements to ensure solutions meet business needs. This role requires the candidate to act as a liaison between our technical team and end users to ensure technical compatibility and satisfaction of deliverables, while writing and maintaining detailed documentation, including business requirements documentation, test plans and test scripts. Experience in the pharmaceutical sector is preferred with knowledge in LLM-based applications for drug discovery.

Responsibilities
• Facilitate business workshops to gather use cases for prioritization.
• Document business requirements and ensure they are understood by the technical team.
• Prioritizing business requirements with workstream leads for review and approval.
• Collaborate with solution architects to ensure traceability between project requirements and solution architecture.
• Work with developers to validate outputs from proof-of-concept solutions, engaging SMEs where needed for input.
• Create test plans and test scripts while also coordinating timelines and SME participation.
• Troubleshooting functional and technical issues in LLM-based applications.
• Collaborating with workstream leads, SMEs and IT team to resolve issues and ensuring solutions are viable and consistent.
• Facilitate and execute end-user acceptance testing following support team processes.
• Assist with deployment planning including cyber risk assessments and change control processes.
• Assist Training Manager with creation of training documentation and communications.
• Prepare and deliver presentations, demos, and walkthroughs for leadership to showcase system capabilities and project progress.
• Be responsive in a fast-paced adapting environment that requires maximum system performance, minimum downtime, and a high degree of customer satisfaction and confidence.
• Other duties as assigned.

Skills
• Understanding of health sciences in context to LLM-based application use cases.
• Must have strong analytical skills and high attention to detail.
• Demonstrate exceptional communication skills with experience delivering presentations to senior leadership.
• A self-starter with demonstrated ability to work independently as well as in cross-country team environment.
• Exceptional interpersonal skills and ability to quickly build and maintain business relationships.
• Can adapt to a constantly changing pharmaceutical environment.
• The ability to quickly learn new system solutions.

Education/Experience
• Must have Bachelor's Degree with a technology focus (IT, Computer Science, or a related field) or a scientific focus (Biology, Chemistry, or a related field)
• Must have 5+ years experience in supporting systems implementation utilizing SDLC and Project Management methodologies.
• Must have 5+ years experience in a dedicated BA role in a project environment.
• Must have 5+ years experience documenting business requirements and test plans for systems implementations in a project environment.
• Prior experience in drug discovery/research is preferred.
• Experience in a client-facing role in a large consulting firm is a plus.
• Expertise in understanding scientific domain context required for LLM-based applications is preferred.
• Experience in supporting LLM-based and non-LLM-based systems is preferred.