Recommendation system development for inventory curation
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 volume prediction
Recommendation system development for inventory curation
Dynamic budget allocation control systems
Ad tech marketplace strategy implementation
Nice-to-have
Experience with advertiser KPI optimization
Ability to translate complex trade-offs into insights
Scalable decisioning logic design skills
Cross-functional collaboration with buyers and partners
Key Requirements
Staff level Applied Scientist experience required
Expertise in causal inference and experimental design