Sr. Manager, Insights & Analytics

Alnylam Pharmaceuticals•Boston, MA

19h•$142,000 - $192,000•Onsite

About The Position

Join the Alnylam Commercial Data and Analytics team and help build a product-driven engine that turns data-driven insights into deployed decisions with measurable business impact. As part of Customer Experience and Innovation, you will work cross-functionally with technical and business partners to accelerate commercial outcomes by standardizing what we build, how we build it, and how we deliver it through the tools and workflows business users rely on every day. As Senior Manager, Advanced Analytics, you will contribute to prioritized Value Teams across the commercial organization: Business Performance & Drivers, Customer & Account Growth, Omnichannel & Media Optimization, Patient Pull-Through & Outcomes, and Evaluation & Learning. Value Teams collaborate fluidly to deliver and manage decision products that enable specific business outcomes, driving competitive foresight and actioning for the commercial organization through predictive and prescriptive analytics. In this role, you will bring strong analytical execution and product thinking to decision-focused analytics. You will work closely with Associate Directors, Directors, and business stakeholders to shape use cases, translate business questions into analytic requirements, deliver high-quality analyses, and help ensure that outputs are adopted in the workflows business users rely on. You will own defined workstreams within and across Value Teams, while contributing reusable analytical components and shared standards that help the broader portfolio move faster. The position will be based in Cambridge, MA.

Requirements

BS degree in Business, Data Science, Computer Science, Engineering, Information Systems, or related field, or equivalent experience.
5+ years in commercial advanced analytics or data science, ideally in healthcare or life sciences.
Strong product-thinking and cross-functional delivery skills, including discovery, prioritization, measurement discipline, and adoption-focused enablement.
Strong proficiency in SQL and Python or R, and comfort with modern analytics tooling such as Spark and dbt for analytics engineering and metric standardization.
Experience with AI and ML frameworks and APIs, and partnering with engineering to productionize analytics and support monitoring.
Experience using AI tooling to improve throughput and quality, including responsible use of agentic AI analytics and AI-native IDEs.
Experience with healthcare data such as EHR, administrative claims, and laboratory data, including common vendors and datasets.

Responsibilities

Work within and across multiple Value Teams, aligning to transparent priorities and delivering reusable analytics that scale across franchises and markets.
Support analytics product strategy and roadmap development for assigned decision products by helping identify high-leverage decisions, size opportunity, sequence releases, and track adoption and impact.
Lead defined analytics workstreams for decision-product use cases such as launch readiness and early performance, competitive dynamics and share shifts, account opportunity prioritization, and digital engagement optimization.
Translate business needs into deployable analytic requirements, including thresholds, segmentation logic, trigger definitions, measurement plans, and learning loops that improve over time.
Deliver decision-ready insights with clear storytelling and stakeholder-ready visuals, including senior stakeholder readouts and materials that support executive-level decision-making.
Partner with engineering to move prototypes into production and define expectations for monitoring, retraining, and operational performance where models are involved.
Partner with Data Stewardship to define and use single source of truth metrics, ensure traceability to governed definitions and gold-layer data products, and contribute to analytics engineering using dbt-informed specifications and documentation.
Use AI tooling to increase efficiency and throughput, including AI-native IDEs and safe use of agentic AI analytics when appropriate; operate in agile delivery rhythms using Jira and GitHub-based version control for analytic artifacts.