Principal Scientist – AI/ML Workflow & Ecosystem Lead (Large Molecule Discovery Informatics) Full-time NEW

Principal Scientist – AI/ML Workflow & Ecosystem Lead (Large Molecule Discovery Informatics)

Let's do this. Let's change the world. In this vital and exciting role, you will serve as a Principal Scientist within the Research organization, leading the integration and scaling of artificial intelligence and machine learning (AI/ML) capabilities across the Large Molecule Discovery (LMD) informatics ecosystem. You will be responsible for translating advanced computational models into robust, production-ready solutions that are embedded within scientific workflows and drive measurable impact across biologics research. As a scientifically grounded leader, you will work closely with experimental and computational scientists to understand research needs and translate them into clear data, workflow, and platform requirements, partnering with technology teams to deliver scalable, production-grade solutions. You will operate at the intersection of science, data, and technology, ensuring that AI/ML capabilities are effectively deployed, adopted, and sustained within a complex R&D environment. This role plays a key role in advancing AI/ML implementation within Large Molecule Discovery (LMD), working collaboratively with enterprise partners to align with and contribute to broader AI/ML strategies. This role leads a horizontally integrated AI enablement team accountable for delivering the foundational capabilities required to scale AI within Large Molecule Discovery.

In this role, your core responsibilities include:

• Lead the transition of AI/ML models from research into production-ready solutions within LMD, ensuring successful integration into domain-specific scientific workflows and informatics systems
• Lead and develop a multidisciplinary team responsible for AI enablement, data standards, and workflow integration within the LMD ecosystem
• Drive adoption of AI/ML capabilities within LMD by defining and implementing research-to-production handoff patterns to implement machine learning models into end-to-end discovery workflows and enabling intuitive, reliable user experiences for scientists
• Provide clear priorities, coaching, and execution oversight to ensure delivery of high-quality, scalable capabilities across a diverse technical team
• Establish best practices for model reproducibility, documentation, governance, and compliance, aligned with enterprise standards and deployment frameworks
• Partner directly with experimental and computational scientists to identify high-impact opportunities for AI/ML integration and ensure solutions align with real-world scientific workflows
• Translate scientific research needs into clear technical, data, and workflow requirements, partnering with technology teams to deliver scalable, production-grade solutions
• Partner across scientific, data, and technology functions to ensure availability of high-quality, ML-ready datasets and robust data pipelines supporting LMD workflows
• Enable standardization of complex biological data (e.g., sequencing and assay data) to support reproducible model training and downstream use in LMD
• Define and advance ontology and metadata frameworks to ensure interoperability and long-term reuse of data across LMD systems and modalities
• Monitor adoption, performance, and impact of deployed AI/ML capabilities within LMD, including development of metrics, dashboards, and reporting
• Lead cross-functional initiatives within LMD and coordinate across stakeholders to ensure alignment, manage scientific dependencies, and deliver integrated solutions at scale
• Collaborate with enterprise AI/ML strategy and platform teams to ensure alignment while representing LMD-specific needs and priorities

We are all different, yet we all use our unique contributions to serve patients. The dynamic professional we seek is a subject matter expert with these qualifications:

• Basic Qualifications:
• Doctorate degree PhD OR PharmD OR MD [and relevant post-doc where applicable] and 5 years of directly related experience
• Or Master's degree and 8 years of directly related experience
• Or Bachelor's degree and 10 years of directly related experience
• Preferred Qualifications:
• PhD with 5+ years of experience applying AI/ML in biotechnology, pharmaceutical, or related R&D environments
• Familiarity with large molecule or biologics discovery workflows
• Demonstrated experience translating computational models into production systems used in scientific or technical workflows
• Experience leading multidisciplinary teams across data, software, and scientific domains
• Strong understanding of machine learning lifecycle management, data engineering, and scalable system integration
• Experience with scientific data platforms, data modeling, and development of ML-ready datasets
• Proven ability to lead complex, cross-functional initiatives in matrixed organizations
• Strong communication skills with the ability to influence and align stakeholders across scientific and technical domains
• Experience defining and measuring adoption and impact of digital or AI-driven capabilities