Staff Applied Scientist

What you'll do

• Build and maintain control systems that dynamically allocate ad budgets across sell-side partners based on quality signals and performance targets
• Design and analyze experiments using causal inference methods to measure the impact of inventory decisions on advertiser KPIs, and develop the metrics that make those trade-offs visible and actionable
• Build the recommendation systems and curation frameworks that help buyers find supply that reaches their target audiences at scale and delivers on their campaign outcomes
• Develop time series models to anticipate supply volume shifts and help buyers and partners plan accordingly
• Partner with engineering, product, and business teams to translate research into scalable, production-ready systems that improve marketplace value for buyers and sellers
• Define the applied science roadmap for the team — identifying high-impact problems, scoping solutions end-to-end, and driving projects from research through production

Requirements

• Advanced degree in data science, statistics, machine learning, economics, applied math, computer science, or a related field
• 7+ years of experience in data science or 5+ years of experience with a PhD
• Experience of owning projects end-to-end (from research to productionization at scale)
• Strong grounding in machine learning, statistics, experimental design, causal inference, and metric development
• Experience with recommendation systems or ranking models
• Experience with large‑scale data processing (e.g., Spark, EMR, Databricks)
• Comfortable with practices that enable reproducible analyses and useful prototypes—clean code, version control, and code review

Tech stack