Sr. Research Systems Analyst

Core Responsibilities

• Administer and optimize core research systems including ELN, LIMS, data management platforms, and other scientific tools including Benchling, CDD Vault, Quilt, AWS, Code Ocean, and Seqera, ensuring reliability, performance, and user satisfaction.
• Collaborate with stakeholders to gather, document, and translate requirements for R&D applications into actionable system configurations and enhancements.
• Provide Tier 2/3 technical support and troubleshooting for research, scientific, and data management systems, serving as an escalation point for complex issues.
• Monitor system performance, coordinate with IT infrastructure teams on uptime and capacity, and manage vendor relationships for supported platforms.
• Implementation & Portfolio Company Onboarding
• Partner directly with Flagship Labs and Pioneering Medicines startups to stand up and configure core research systems, ensuring new companies are up and running quickly on platforms suited to their research needs.
• Implement system configurations, data structures, and integrations tailored to each company's stage and scientific priorities.
• Train startup teams on platform best practices and workflows so they can operate independently from day one.
• Proactively identify opportunities for future integrations and platform enhancements as portfolio companies grow and their needs evolve.
• AI Features & Usage
• Maintain working knowledge of the AI features available within core research platforms, and stay current as new capabilities are released.
• Develop practical use cases and recommendations that help researchers and portfolio companies get the most value out of each platform's AI features.
• Advise on when a platform's native AI features are sufficient versus when a deeper integration may be worth pursuing.
• Training, Documentation & User Enablement
• Create high-quality user documentation, training materials, and quick-reference guides for research systems.
• Design and deliver training sessions and onboarding programs that drive system adoption and empower researchers to work effectively with digital tools.
• Develop and maintain a knowledge base that captures best practices, FAQs, and enablement resources for research systems across Pioneering Medicines and Flagship Labs companies.
• Continuous Improvement & Innovation
• Stay current with developments in laboratory informatics and cloud-native data management relevant to life sciences research platforms.
• Proactively identify opportunities to improve system usability, data quality, and researcher productivity, and contribute to roadmap planning discussions.
• Support process improvement initiatives and contribute to IT project delivery across the Research Systems team.
• Cross-Functional Collaboration
• Work closely with laboratory researchers, bioinformatics engineers, data architects, and IT colleagues to deliver integrated solutions that align with scientific priorities.
• Act as a liaison between research end users and IT, translating scientific needs into system requirements and ensuring technology investments deliver measurable value.

Required Qualifications

• Bachelor's degree in Information Technology, Computer Science, Bioinformatics, or a life sciences-related field; equivalent experience considered.
• At least 5 years of experience supporting, configuring, or administering laboratory or data management systems within the life sciences industry.
• Hands-on experience with one or more core research platforms (e.g., Benchling, CDD Vault, Quilt, AWS, Code Ocean, Seqera, or similar ELN/LIMS/data management tools).
• Familiarity with AI/ML concepts and an interest in how AI features are being embedded into enterprise research platforms.
• Demonstrated ability to create clear technical documentation, training materials, and user-facing guidance for diverse audiences.
• Strong analytical and problem-solving skills, with the ability to assess platform capabilities and translate them into actionable recommendations.
• Excellent communication skills with the ability to convey complex technical concepts to non-technical stakeholders effectively.
• Prominent ability to manage multiple concurrent priorities in a fast-paced, science-driven environment.

Prior Experience

• Familiarity with prompt engineering, LLM integration patterns, or AI feature evaluation for enterprise tools.
• Experience with scientific programming languages (e.g., Python, SQL) for data integration, reporting, or automation.
• Knowledge of cloud-based data management platforms (AWS S3, Glue, Athena) and FAIR data principles.
• Experience in a biotech, pharmaceutical, or academic research environment with cross-functional team engagement.