Overview  

Alnylam is seeking a deeply technical and strategic leader to serve as Senior Director, Research & Early Development (ReDev) Technology. This role owns the domain-specific scientific systems architecture, computational platforms, and AI-enabled digital capabilities supporting Target/Biomarker Discovery, RNAi Platform & Delivery, Screening, Lead Optimization, DMPK, Bioanalytics, Investigative Sciences, and Non-Clinical Safety. 

This is a strategic technology leadership role that also requires meaningful architectural depth. The Senior Director must partner closely with Research & Early Development leaders at all levels while actively guiding domain system architecture, integration patterns, modern data design, AI-enabled workflow modernization, secure scientific computing environments, and multi-omics/imaging data integration. 

The role requires familiarity with large-scale human genetics and cohort datasets (e.g., UK Biobank, FinnGen, NASHBio) and the ability to design systems that enable their effective utilization for target discovery, validation, and translational research. 

The Senior Director will serve as the IT representative within the Research & Early Development Leadership Team, contributing to strategic discussions and ensuring digital priorities are tightly aligned with scientific and portfolio objectives. 

The role also requires close collaboration with Clinical Development and CMC organizations to ensure discovery and preclinical digital capabilities are aligned with downstream clinical, manufacturing, and regulatory requirements — enabling seamless lifecycle data continuity from target discovery through clinical development and CMC readiness. 

Shared lab execution platforms (e.g., ELN, LIMS) are owned by the Lab-to-Product Platform & Operations organization. Enterprise data engineering and centralized AI platforms are owned by the Enterprise Data & AI organization. This role partners closely with both to ensure ReDev domain systems are integrated, AI-ready, and aligned with enterprise standards. 

This hybrid role is based in Cambridge, MA with regular engagement at our Cambridge headquarters. 

Key Responsibilities 

Own the ReDev Domain Architecture & Application Strategy 

• Define and govern the technical architecture across discovery and preclinical domain systems 

• Lead lifecycle management of applications supporting: 

• • Molecular design and modeling 

• • RNAi delivery and screening platforms 

• • Digital pathology and imaging systems 

• • DMPK, bioanalytics, and nonclinical safety 

• • Omics, spatial omics, proteomics, and high-throughput platforms 

• Enable effective integration and analysis of large-scale human genetics and cohort datasets (e.g., UK Biobank, FinnGen, NASHBio) 

• Ensure domain systems integrate effectively with shared ELN/LIMS and enterprise data platforms 

Partner on Domain Data Architecture & Snowflake Integration 

• Partner with Enterprise Data & AI to design domain data flows into Alnylam’s Snowflake-based enterprise data platform 

• Define domain data requirements for genomics, proteomics, spatial omics, and imaging datasets 

• Enable structured data products for human genetics and translational research 

• Collaborate on metadata alignment, ontology mapping, and cross-cohort harmonization 

• Enterprise Data & AI owns core Snowflake platform engineering and infrastructure 

Partner on Generative & Agentic AI-Enabled Scientific Systems 

• Partner with Enterprise Data & AI to design and operationalize AI-enabled workflows across discovery and preclinical domains 

• Apply generative AI to support hypothesis generation, target identification, and multi-omics interpretation 

• Architect agentic AI systems capable of orchestrating cross-dataset analyses (e.g., cohort genetics + proteomics + imaging) 

• Ensure AI capabilities are embedded into production scientific workflows 

• Enterprise Data & AI owns core AI platform engineering and model infrastructure 

Architect Secure Scientific Computing & Trusted Research Environments 

• Design and operate Trusted Research Environments (TREs) to support secure analysis of sensitive human genetics and cohort data 

• Enable controlled-access environments for datasets such as UK Biobank, FinnGen, and NASHBio 

• Ensure compliance with privacy, governance, and regulatory standards 

• Partner with Enterprise Data, Security, and Infrastructure teams to align on enterprise security controls 

Serve as Strategic & Technical Thought Partner 

• Act as primary IT representative to the Research & Early Development Leadership Team 

• Contribute to portfolio and target strategy discussions with a technology and data perspective 

• Translate scientific strategy into scalable digital and AI-enabled architecture 

• Represent IT in cross-functional and executive leadership forums 

• Partner with Clinical and CMC technology leaders to ensure discovery and preclinical systems enable translational continuity, biomarker integration, clinical readiness, and downstream manufacturing alignment 

• Contribute to cross-functional lifecycle architecture discussions spanning ReDev, Clinical, and CMC 

Required Qualifications 

• 15+ years of progressive IT leadership supporting drug discovery and preclinical development 

• Demonstrated architectural depth across complex scientific systems 

• Proven ability to partner effectively with senior scientific stakeholders 

• Experience operating within senior leadership forums (e.g., R&D Leadership Teams) with demonstrated executive presence 

Human Genetics & Multi-Omics Expertise 

• Familiarity with large-scale population and cohort datasets such as: 

• • UK Biobank 

• • FinnGen 

• • NASHBio 

• Demonstrated understanding of how these datasets are utilized for: 

• Target discovery and validation 

• Rare variant association 

• Translational research 

• Experience supporting or integrating: 

• Genomics and variant datasets 

• Spatial omics platforms 

• Proteomics datasets 

• Imaging-genomics analyses 

Domain Architecture & Data Engineering Fluency 

• Deep expertise in modern data engineering concepts: 

• ELT/ETL patterns 

• Lake/lakehouse architectures 

• API-first integration 

• Cloud-native data platforms 

• Experience integrating domain scientific systems with enterprise data platforms (e.g., Snowflake) in partnership with centralized teams 

• Advanced SQL and Python proficiency with production-quality engineering standards 

AI & Secure Computing Experience 

• Demonstrated practical experience applying both generative AI and agentic AI in scientific or R&D environments 

• Experience operationalizing AI within production discovery workflows 

• Hands-on experience designing and implementing Trusted Research Environments (TREs) 

• Strong understanding of responsible AI and secure scientific computing 

Preferred Qualifications 

Experience with modern discovery and preclinical technology ecosystems, including: 

• Digital Pathology & Imaging: HALO, HALO Link, Patholytix, Visiopharm 

• Non-Clinical Safety & DMPK: SEND environments, Phoenix (WinNonlin) 

• Bioanalytics Platforms: Benchling, Sapio 

• Omics & Workflow Engines: Paradigm4, DNAnexus, AWS HealthOmics, Nextflow 

• High-Content Imaging: Signals Image Artist 

Experience in RNAi, oligonucleotide, or gene-modality platforms strongly preferred.