Data-Driven Marketing Optimization Specialist
5 days ago
As a Data-Driven Marketing Optimization Specialist at Wayfair, you will play a critical role in driving business growth through data science and machine learning. We are seeking a highly motivated and experienced professional to join our Bidding and Decisioning team in Boston or Berlin.
Estimated Salary: $150,000 - $190,000 per year (based on US location)
About the RoleYour primary responsibility will be to build and implement machine learning solutions to enhance efficiency, defining problem statements, and delivering against technical and research objectives in a timely manner. You will collaborate with cross-functional teams to align roadmaps, adopt best practices, and build scalable, sustainable bidding solutions.
- You will identify new opportunities and evolve the spend management optimization framework, expanding its coverage for new use cases.
- Collaborate closely with engineering, infrastructure, and ML platform teams to adopt best practices for building and deploying scalable ML services.
To succeed in this role, you will need:
- Bachelor's or advanced degree (Master's, PhD) in Computer Science, Economics, Mathematics, Statistics, or related field.
- 3-6 years of experience as an ML engineer, applied scientist, or research scientist, with a proven track record of delivering ML projects autonomously and driving measurable business impact, particularly in digital marketing decisioning (e.g., spend management, content optimization, segmentation) and/or ML decisioning systems.
- Experience with end-to-end project ownership, including collaboration with business partners and strong written and verbal communication skills.
- Strong SQL skills and excellent software engineering skills in Python.
Why Join Us?
- Be part of a dynamic and innovative company that is shaping the future of e-commerce.
- Collaborate with talented professionals who share your passion for machine learning and data science.
- Pursue cutting-edge research and development opportunities in a supportive and collaborative environment.
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