Staff Applied Scientist

The Trade Desk is a global technology company and the world’s leading independent platform for digital advertising, with nearly 4,000 employees across more than 30 offices. Our technology helps advertisers reach the right audiences across the open internet — from streaming TV and podcasts to mobile apps, news, and more.

Advertising powers the content people love. By making it more transparent, effective, and responsible, we help support trusted journalism, quality entertainment, and creators worldwide. The world’s brands and agencies rely on us to reach their customers and grow their businesses responsibly.

The scale of our platform brings unique technical challenges — from processing massive datasets in real time to building systems that operate reliably on a global scale. When you work here, your impact is worldwide. We welcome diverse perspectives, encourage curiosity, and build teams that learn from one another. If you’re driven to solve meaningful challenges, we’d love to meet you.

About the Team

The Deals and Inventory Marketplaces R&D team is responsible for the technology, data science, and operational frameworks that power The Trade Desk's programmatic inventory marketplace and deals management platform. We build the systems and science that help buyers curate the supply they want, understand the impact of their decisions, and execute against their strategies with precision. Applied scientists work closely with engineering, product, and business stakeholders to ship solutions that are rigorous and directly tied to business outcomes.

Our work spans three interconnected areas:

Inventory Execution: building the control systems, supply/demand forecasting models, and decisioning logic that enable buyers to execute their customized strategies efficiently and at scale.

Marketplace Curation: developing recommendation systems and curation frameworks that help buyers construct customized inventory strategies balancing audience relevance, publisher quality, and scale.

Inventory Science: designing the measurement frameworks and causal methods that quantify the impact of inventory decisions on advertiser outcomes, and translate complex trade-offs into actionable insight.

Responsibilities

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.

Who You Are

We're looking for a Staff Applied Scientist who thrives at the intersection of rigorous methodology and real-world systems. You are comfortable moving between statistical modeling, experiment design, and ML, and you know how to translate that work into scalable solutions that have a measurable impact on how billions of ad dollars are spent. You care about getting things right, and you're motivated by problems where the stakes are high and the data is complex.

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

Nice to Have

Experience in programmatic advertising, real-time auctions, or supply-side systems

Familiarity with control theory, constrained optimization, or budget allocation systems

Experience building or applying agentic AI workflows, including the use of LLMs for automation, decision support, or data-driven product features

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