Staff Applied Scientist

Our work spans three interconnected areas:

• Marketplace Execution: building the control systems, supply prediction 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 marketplace 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
  
  

The Trade Desk does not accept unsolicited resumes from search firm recruiters. Fees will not be paid in the event a candidate submitted by a recruiter without an agreement in place is hired; such resumes will be deemed the sole property of The Trade Desk. The Trade Desk is an equal opportunity employer. All aspects of employment will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.

As an Equal Opportunity Employer, The Trade Desk is committed to creating an inclusive hiring experience where everyone has the opportunity to thrive.

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