Sr. Manager, Experimentation (Technical)

Data is part of the DNA at NBCUniversal, and the Experimentation team plays a large role in driving our data driven culture. We own the technology that enables experimentation and act as subject matter experts to help teams measure the performance of the features and campaigns that customers experience every day. Our work is high visibility as it directly informs product, advertisement, marketing, and merchandising decisions.The Sr. Manager, Experimentation (Technical) will own the statistical frameworks and processes that power our analytics and operational solutions to support self-service insights and decisioning within the organization. The team’s output enables automated dashboards and exploratory analysis that will be used by Product, Marketing, Engineering and Merchandising stakeholders to monitor performance and generate insights from experiments. This role will manage a team and work closely with other Data and Decision Science partners to define best practices and continuously improve statistical methods of how we analyze and share experiment results.Responsibilities include, but are not limited to:Develop statistical frameworks and processes designed to analyze experiments to enable fast, reliable and accurate decision-making for product, marketing, ads and marketing teamsEnhance experiment analysis methodologies, including Bayesian and frequentist approaches, variance reduction techniques, and sequential testingCollaborate with engineering to integrate statistical algorithms and new metrics into our experimentation platformDevelop automated solutions for experiment diagnostics, anomaly detection, and guardrail monitoringStay ahead of industry advancements, improving our statistical engine with the latest methodologies and best practicesCommunicate statistical concepts to stakeholders, including product managers, analysts, and engineering teamsLead Center of Excellence for Experimentation – Define best practices and frameworks for trustworthy online controlled experiments; drive cultural adoption of data-driven decision making across product and engineering teams.