Sr. Applied Scientist

Overview Are you interested in defining the science strategy that enables Amazon to market to millions of customers based on their lifecycle needs rather than one-size-fits-all campaigns?We are seeking a Senior Applied Scientist to lead the science strategy for our Lifecycle Marketing Experimentation roadmap within the PRIMAS (Prime & Marketing Analytics and Science) team. The position is open to candidates in Amsterdam and Barcelona. In this role, you will own the end-to-end science approach that enables EU marketing to shift from broad, generic campaigns to targeted, cohort-based marketing that changes customer behavior. This is a high-ambiguity, high-impact role where you will define what problems are worth solving, build the science foundation from scratch, and influence senior business leaders on marketing strategy. You will work directly with Business Directors and channel leaders to solve critical business problems: how do we win back customers lost to competitors, convert Young Adults to Prime, and optimize marketing spend by de-averaging across customer cohorts.ResponsibilitiesOwn the end-to-end science strategy for lifecycle marketing, defining the roadmap across audience targeting, behavioral modeling, and measurementNavigate high ambiguity in defining customer journey frameworks and behavioral models – our most challenging science problem with no established playbookLead strategic discussions with business leaders translating business needs into science solutions and building trust across business and tech partnersMentor and guide a team of 2-3 scientists and BIEs on technical execution while contributing hands-on to the hardest problemsAdvanced Customer Behavior ModelingBuild sophisticated propensity models identifying customer cohorts based on lifecycle stage and complex behavioral patterns (e.g., Bargain hunters, Young adults Prime prospects)Define customer journey frameworks using advanced techniques (Hidden Markov Models, sequential decision-making) to model how customers transition across lifecycle stagesIdentify which customer behaviors and triggers drive lifecycle progression and what messaging/levers are most effective for each cohortIntegrate 1P behavioral data with 2P survey insights to create rich, actionable audience definitionsMeasurement & Cross-Workstream IntegrationPartner with measurement scientist to design experiments (RCTs) that isolate audience targeting effects from creative effectsEnsure audience definitions, journey models, and measurement frameworks work coherently across Meta, LiveRamp, and owned channelsEstablish feedback loops connecting measurement insights back to model improvementsAbout The TeamThe PRIMAS (Prime & Marketing Analytics and Science) is the team that support the science & analytics needs of the EU Prime and Marketing organization, an org that supports the Prime and Marketing programs in European marketplaces and comprises 250-300 employees. The PRIMAS team is part of a larger tech team of 100+ people called WIMSI (WW Integrated Marketing Systems and Intelligence). WIMSI core mission is to accelerate marketing technology capabilities that enable de-averaged customer experiences across the marketing funnel: awareness, consideration, and conversion.Basic QualificationsExperience working with and influencing senior level stakeholdersExperience working effectively with science, data processing, and software engineering teamsExperience in customer lifecycle marketing or partner marketing managementExperience building machine learning models or developing algorithms for business applicationExperience building and effectively executing a strategy from the ground up, including designing roadmaps to drive incremental progress towards long-term vision and goalsPhD in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science, or experience in data science, machine learning or data mining or equivalent research experiencePreferred QualificationsExperience with advanced modeling techniques (Markov models, sequential models, causal inference)Experience with experimental design principles and causal inference techniquesTechnical leadership – ability to mentor scientists while contributing hands-on to complex modeling challengesAmazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice to know more about how we collect, use and