Job Information
Meta Marketing Science Partner in London, United Kingdom
Summary:
Meta's Marketing Science team is seeking a Marketing Science Partner to help advertisers measure and improve the effectiveness of their campaigns across Meta's family of apps and services. In this role, you will work directly with advertisers and internal sales teams to design and execute measurement strategies, including incrementality testing, attribution analysis, and brand and conversion lift studies. You will translate complex data findings into actionable recommendations that drive better advertising outcomes, and you will champion the adoption of Meta's measurement products and methodologies across client portfolios.
Required Skills:
Marketing Science Partner Responsibilities:
Partner with advertisers and internal sales teams to develop and operationalize measurement learning agendas that connect advertising activity to business outcomes
Design and execute incrementality experiments, including conversion lift and brand lift studies, to quantify the causal impact of Meta advertising campaigns
Consult with clients on measurement feasibility, hypothesis formation, and interpretation of results from ad effectiveness studies
Analyze large advertising datasets using statistical tools to surface patterns, validate measurement approaches, and generate actionable insights
Translate complex measurement findings into clear, audience-appropriate narratives for client stakeholders across varying levels of analytical sophistication
Drive adoption of preferred measurement methodologies, best practices, and Meta measurement products within assigned client verticals
Collaborate with Meta product, research, and partnerships teams to pilot and scale new measurement capabilities tailored to advertiser needs
Facilitate internal and external education workshops on measurement best practices, including experimental design, attribution, and auction dynamics
Support clients in coordinating with third-party measurement vendors to ensure study design integrity and data quality
Identify opportunities to align measurement strategy with client business priorities and provide input into team-level measurement goals and vertical roadmaps
Minimum Qualifications:
Minimum Qualifications:
2+ years of experience designing or executing advertising measurement projects, including field experiments, lift studies, or attribution analyses
2+ years of experience analyzing and manipulating large datasets using SQL, R, Python, or comparable statistical tools to generate insights
Experience communicating quantitative research findings and measurement recommendations to non-technical client or business stakeholders
Experience working cross-functionally with sales, product, or research teams to deliver client-facing analytical solutions
Experience with digital advertising measurement concepts such as incrementality testing, multi-touch attribution, media mix modeling, or experimental design
Preferred Qualifications:
Preferred Qualifications:
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
Experience working with advertisers in performance-driven verticals such as e-commerce, app technology, or gaming, with familiarity in mobile measurement and app campaign optimization
Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
Experience designing or evaluating panel-based or survey-based brand measurement studies in a digital advertising context
Familiarity with AI-enabled analytical workflows and modern AI or large language model tools applied to data analysis or insight generation
Experience collaborating with third-party measurement vendors or industry bodies on the development or validation of advertising effectiveness methodologies
Industry: Internet