Empleos de data scientist

DESCRIPTION

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.

Key job responsibilities
Science Strategy & Leadership:

1. Own the end-to-end science strategy for lifecycle marketing, defining the roadmap across audience targeting, behavioral modeling, and measurement
2. Navigate high ambiguity in defining customer journey frameworks and behavioral models – our most challenging science problem with no established playbook
3. Lead strategic discussions with business leaders translating business needs into science solutions and building trust across business and tech partners
4. Mentor and guide a team of 2-3 scientists and BIEs on technical execution while contributing hands-on to the hardest problems

Advanced Customer Behavior Modeling:

1. Build sophisticated propensity models identifying customer cohorts based on lifecycle stage and complex behavioral patterns (e.g., Bargain hunters, Young adults Prime prospects)
2. Define customer journey frameworks using advanced techniques (Hidden Markov Models, sequential decision-making) to model how customers transition across lifecycle stages
3. Identify which customer behaviors and triggers drive lifecycle progression and what messaging/levers are most effective for each cohort
4. Integrate 1P behavioral data with 2P survey insights to create rich, actionable audience definitions

Measurement & Cross-Workstream Integration:

1. Partner with measurement scientist to design experiments (RCTs) that isolate audience targeting effects from creative effects
2. Ensure audience definitions, journey models, and measurement frameworks work coherently across Meta, LiveRamp, and owned channels
3. Establish feedback loops connecting measurement insights back to model improvements

About the team
The 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 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 QUALIFICATIONS

• Experience working with and influencing senior level stakeholders
• Ph.D. 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
• Experience working effectively with science, data processing, and software engineering teams
• Experience in customer lifecycle marketing or partner marketing management
• Applied Scientist with 7+ years of experience in applied machine learning and customer analytics
• Track record of defining science strategy for new problem spaces

PREFERRED QUALIFICATIONS

• Experience with advanced modeling techniques (Markov models, sequential models, causal inference)
• Experience with experimental design principles and causal inference techniques
• Technical leadership – ability to mentor scientists while contributing hands-on to complex modeling challenges

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