Measurement Science Partner, Global Accounts

The Measurement team is responsible for demonstrating and improving the effectiveness of advertising on TikTok. We partner with advertisers, agencies, and cross-functional teams to interpret campaign performance, shape measurement strategies, and unlock stronger business outcomes through data-driven insights. Sitting at the intersection of sales, product, analytics, and marketing, we help clients understand how TikTok drives results across branding and performance objectives, while also influencing the evolution of measurement products, solutions, and partnerships.

About the role As a Measurement Science Partner, you'll work at the frontier of causal measurement, experimentation, and AI-led scalable analytics, turning bespoke analysis into reusable measurement solutions that prove TikTok's effectiveness to the world's largest advertisers. This is a hands-on IC role within Measurement, sitting at the intersection of rigorous quantitative execution and trusted internal partnership. Working alongside our client-facing Measurement Partners, you'll develop and scale the analytical frameworks and production-ready tools that drive global revenue growth across a portfolio of strategic, high-visibility accounts.

The ideal candidate brings deep quantitative execution, a consultative mindset, and the ability to turn complex statistical models into scalable solutions that empower our broader measurement organization and prove TikTok's effectiveness to the world's largest advertisers.

Responsibilities - Apply rigorous statistical models and causal inference methodologies, including experimentation (brand lift, conversion lift, geo lift), incrementality testing, attribution, and media mix modeling, to campaign and business outcome data to explain the underlying drivers of upper and lower-funnel metrics - Build and ship production-ready analytical tools, models, and data applications in Python and/or R that are robust, repeatable, documented, and built for reuse across teams and accounts - Partner closely with client-facing Measurement Partners to develop and implement advanced measurement strategies that directly support business and revenue growth for our largest global advertisers - Train and enable measurement teams on how to effectively use your reporting frameworks and analytical solutions, and collaborate with Sales, Measurement Product, and Data Science to inform future measurement infrastructure and GTM strategies

Minimum Qualifications: - Bachelor's degree in a quantitative field (Statistics, Data Science, Mathematics, Operations Research) - 5+ years of experience in data science, marketing science, or digital measurement with a heavy emphasis on statistical modeling - Strong hands-on skills in SQL and Python and/or R, with experience building reusable analyses, dashboards, or analytical applications in production or near-production settings - Experience applying advanced methods such as regression, decision trees, random forests, causal inference, or similar approaches to large-scale campaign and business datasets - Strong communication skills with the ability to translate technical findings clearly for both technical and non-technical stakeholders - Demonstrated ability to work cross-functionally with sales, product, and measurement stakeholders in a fast-paced, high-ambiguity environment

Preferred Qualifications: - Graduate degree preferred - Proven experience with experimental design (brand lift, conversion lift, geo lift), attribution frameworks, and media mix modeling applied to real campaign data - Familiarity with privacy-safe measurement, incrementality testing, or auction and optimization analyses in digital advertising - Experience supporting large, strategic, or global advertiser accounts in a consultative environment where analytical outputs must influence senior stakeholders - Track record of converting one-off analyses into reusable frameworks or scaled analytical products for broader organizational use - Experience training or enabling commercial or partner-facing teams on analytical workflows and tooling