Machine Learning Scientist / Applied Scientist, EU Prime and Marketing Analytics & Science (PRIMAS)

Amazon.com, Inc.
Barcelona, Spain
2 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English

Job location

Barcelona, Spain

Tech stack

Java
Business Software
C++
Data Mining
Data Structures
Distributed Systems
Python
Machine Learning
Parsing
Software Engineering
Information Technology
Programming Languages

Job description

Are you interested in building the measurement foundation that proves whether targeted, cohort-based marketing actually changes customer behavior at Amazon scale?, We are seeking an Applied Scientist to own measurement and experimentation for our Lifecycle Marketing Experimentation roadmap within the PRIMAS (Prime & Marketing Analytics and Science) team. In this role, you will design and execute rigorous experiments that measure the effectiveness of audience-based marketing campaigns across multiple channels, providing the evidence that guides marketing strategy and investment decisions.

This is a high-impact role where you will build measurement frameworks from scratch, design experiments that isolate causal effects, and establish the experimental standards for lifecycle marketing across EU. You will work closely with business leaders and the senior science lead to answer critical questions: does targeting specific cohorts (Bargain hunters, Young adults) improve efficiency vs. broad campaigns? Which creative strategies drive behavior change? How should we optimize marketing spend across channels?

Key job responsibilities Measurement & Experimentation Ownership:

  1. Own measurement end-to-end for lifecycle marketing campaigns - design experiments (RCTs, geo-tests, audience holdouts) that measure campaign effectiveness across marketing channels
  2. Build measurement frameworks and experimental best practices that work across different activation platforms and can scale to multiple campaigns
  3. Establish experimental standards and tooling for lifecycle marketing, ensuring statistical rigor while balancing business constraints

Causal Inference & Analysis:

  1. Apply causal inference methods to measure incremental impact of marketing campaigns vs. counterfactual
  2. Navigate measurement challenges across different platforms (Meta attribution, LiveRamp, clean rooms, onsite tracking)
  3. Analyze experiment results and provide optimization recommendations based on statistical evidence
  4. Establish guardrails and success criteria for campaign evaluation

Requirements

PhD in computer science, machine learning, engineering, or related fields

  • Experience in patents or publications at top-tier peer-reviewed conferences or journals
  • Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
  • Experience building machine learning models or developing algorithms for business application
  • Experience with programming languages such as Python, Java, C++

Preferred Qualifications

  • Experience in professional software development
  • Experience in designing experiments and statistical analysis of results

About the company

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., Amazon 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 (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.

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