The role involves building control systems that dynamically allocate ad budgets across sell-side partners based on quality signals and performance targets
Job Summary
The role involves building control systems that dynamically allocate ad budgets across sell-side partners based on quality signals and performance targets.
Candidates will design experiments using causal inference methods to measure the impact of inventory decisions on advertiser KPIs.
The position requires developing time series models to anticipate supply volume shifts and help buyers plan accordingly.
Matching Summary
The role involves building control systems that dynamically allocate ad budgets across sell-side partners based on quality signals and performance targets.
Skills & Requirements
Must-have
Causal inference methods for experiment analysis
Time series modeling for supply prediction
Recommendation system development
Control systems for budget allocation
Measurement frameworks for advertiser outcomes
Nice-to-have
Experience with large-scale data platforms
Ability to translate trade-offs into insights
Background in supply chain optimization
Key Requirements
Advanced degree in Statistics, Computer Science, or related field
Expertise in machine learning and statistical modeling
Proven experience with causal inference techniques