The Trade Desk
Staff Applied Scientist
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Staff Applied Scientist
Staff Applied Scientist, Marketplace Science (Advertising Optimization & Recommendations)
Our work spans three interconnected areas:
- Marketplace Execution: Building control systems, supply prediction models, and decisioning logic to enable buyers to execute customized strategies efficiently at scale.
- Marketplace Curation: Developing recommendation systems and curation frameworks to help buyers construct customized inventory strategies balancing audience relevance, publisher quality, and scale.
- Inventory Science: Designing measurement frameworks and causal methods to quantify the impact of marketplace decisions on advertiser outcomes.
Responsibilities
- Build and maintain control systems for dynamic ad budget allocation across sell-side partners, driven by quality signals and performance targets.
- Design and analyze experiments using causal inference methods to measure inventory decisions’ impact on advertiser KPIs.
- Develop recommendation systems and curation frameworks to aid buyers in discovering supply that aligns with target audiences at scale.
- Build time-series models to predict supply volume shifts for strategic planning.
- Collaborate across engineering, product, and business teams to operationalize research into scalable, production-ready systems.
- Define the applied science roadmap, addressing high-impact challenges from research to deployment.
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Who You Are
We seek a Staff Applied Scientist who thrives at the intersection of rigorous methodology and high-impact systems. You should:
- Bridge statistical modeling, ML, and experimental design to apply solutions at scale.
- Care deeply about accuracy and work where challenges are complex and consequential.
- Translate data-driven insights into actionable systems managing billions in ad spend.
Requirements
- Advanced degree in data science, statistics, ML, economics, applied math, or computer science.
- 7+ years of data science experience (or 5+ with a PhD) with end-to-end project ownership.
- Proficiency in ML, statistics, experimental design, causal inference, and metric development.
- Experience with recommendation systems and ranking models.
- Experience with large-scale data processing (e.g., Spark, EMR, Databricks).
- Practices for reproducible analysis (clean code, version control, code reviews).
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Nice-to-Have:
- Background in programmatic advertising, real-time auctions, or supply-side systems.
- Competence in control theory, constrained optimization, or budget allocation.
- Experience leveraging agentic AI (LLMs) for decision-making, automation, or product features.
Notes on Employment The Trade Desk follows strict hiring policies, including affirmative action compliance, fee prohibitions for unsolicited recruiters, and non-discriminatory practices. Accommodations for applicants are available upon request.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
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