Associate Director, Multimodal Discovery Platform

Alnylam Pharmaceuticals•Cambridge, MA

10d•$172,200 - $233,000•Hybrid

About The Position

Alnylam is seeking a highly technical and delivery-oriented leader to serve as Associate Director, Multimodal Discovery Platforms. This role is responsible for the IT product ownership, architecture alignment, technology delivery, and operational stewardship of digital platforms supporting multimodal discovery workflows across Research & Early Development (ReDev). The Associate Director will lead technology solutions supporting Omics, Spatial Omics, Digital Pathology, High-Content Imaging, Flow Cytometry, and Medical Imaging. The role partners closely with scientists, software engineers, data engineers, architects, and external vendors to deliver scalable, integrated, and AI-ready solutions that accelerate target discovery, biomarker research, translational science, RNAi discovery and delivery, and preclinical development. This position requires a leader who can bridge science and technology while demonstrating strong architecture and software engineering fluency. The successful candidate must be able to understand complex scientific workflows, translate business needs into technology requirements, evaluate technical design options, and partner effectively with architects and engineers to deliver scalable and sustainable solutions. IT Data & AI owns enterprise data engineering, Snowflake, and enterprise data/AI platforms. Lab-to-Product Platform & Operations Technology owns ELN, generic LIMS, and laboratory execution platforms. This role partners closely with both teams to ensure multimodal discovery platforms are integrated, interoperable, and aligned with enterprise standards.

Requirements

Bachelor's degree in Computer Science, Bioinformatics, Computational Biology, Engineering, Life Sciences, or related field (or equivalent experience).
10+ years of experience supporting scientific informatics, discovery technology, computational biology platforms, imaging informatics, or related domains within biotech or pharmaceutical organizations.
Experience delivering technology solutions supporting one or more of the following domains: Omics, Spatial omics, Digital pathology, High-content imaging, Flow cytometry, Medical imaging.
Strong understanding of modern software architecture, cloud-native platforms, API-first integration, and scientific data ecosystems.
Demonstrated ability to translate scientific workflows into technology requirements and partner with architects and engineers to deliver scalable solutions.
Experience leading cross-functional initiatives involving scientists, software engineers, data engineers, and external vendors.
Working knowledge of software engineering practices, including SDLC, testing strategies, CI/CD, and platform observability.
Strong leadership, communication, and stakeholder engagement skills.

Nice To Haves

Advanced degree in Bioinformatics, Computational Biology, Computer Science, Engineering, or Life Sciences.
Experience with platforms such as: HALO, HALO Link, Visiopharm, Patholytix, Signals Image Artist, FlowJo, OMERO, DNAnexus, Paradigm4, AWS HealthOmics, Lifebit.
Experience supporting genomics, transcriptomics, proteomics, spatial biology, or imaging-based discovery workflows.
Proficiency with Python, SQL, Snowflake, Databricks, and modern scientific computing environments.
Experience with cloud-native architectures and large-scale scientific data platforms.
Familiarity with machine learning, image analytics, and AI-enabled scientific workflows.
Experience in RNAi, oligonucleotide therapeutics, or advanced modality research environments.

Responsibilities

Lead Multimodal Discovery Technology Platforms: Own the lifecycle management, roadmap, and delivery of digital platforms supporting Omics and multi-omics, Spatial omics, Digital pathology, High-content imaging, Flow cytometry, Medical imaging.
Define technology roadmaps aligned with scientific priorities and business outcomes.
Partner with Scientists to Enable RNAi Discovery and Delivery: Collaborate with research and preclinical development teams to understand evolving scientific needs.
Translate scientific workflows into scalable technology solutions and implementation plans.
Drive adoption of digital capabilities that improve scientific productivity, data quality, and reproducibility.
Solution Delivery, Architecture & Engineering Partnership: Partner with enterprise and domain architects to define solution architectures that support multimodal discovery workflows.
Provide scientific and business context in partnership with business stakeholders to guide architecture, integration, and technology design decisions.
Collaborate with software engineering, data engineering, infrastructure, and security teams to ensure solutions are scalable, secure, and maintainable.
Review vendor architectures, technical designs, and integration approaches to ensure alignment with business requirements and enterprise standards.
Contribute to technology roadmaps, architecture reviews, and platform modernization initiatives.
Ensure multimodal discovery platforms are effectively integrated with enterprise data, AI, infrastructure, security, and laboratory technology ecosystems.
Enable Multimodal Data Workflows: Design and deliver workflows supporting acquisition, processing, analysis, and visualization of multimodal datasets.
Enable integration across imaging, pathology, omics, and flow cytometry ecosystems.
Partner with IT Data & AI team and ReDev scientists to ensure multimodal data is accessible, governed, and AI-ready.
Drive Platform Integration & Automation: Lead integration initiatives across scientific applications, laboratory platforms, data environments, and analytics tools.
Define API-first integration patterns and scalable data exchange mechanisms.
Identify opportunities to automate scientific workflows and reduce manual processes.
Support AI-Enabled Scientific Workflows: Partner with IT Data & AI teams to enable AI and machine learning use cases across multimodal discovery domains.
Identify opportunities to leverage generative/agentic AI, omics/image analytics, and advanced computational methods.
Ensure multimodal platforms support future AI-enabled scientific workflows.
Operational Excellence & Governance: Establish governance for demand intake, prioritization, release management, and platform lifecycle management.
Ensure platforms are secure, scalable, reliable, and support scientific business continuity.
Define platform support models, service levels, and performance metrics.
Partner with business stakeholders to support user adoption, training, communications, and value realization.
Monitor platform utilization, user feedback, and business outcomes to inform roadmap and investment decisions.