Staff Applied Scientist , Inventory & Marketplace Quality

Responsibilities

• Technical Ownership
• Own the data science vision and roadmap for MQE initiatives, aligning technical direction with strategic goals and industry trends.
• Design, build, and productionize statistical and machine learning models to detect fraud, invalid traffic, and low-quality inventory in adversarial, large-scale environments.
• Define and maintain metrics, experimentation procedures, and monitoring systems to ensure long-term performance.
• Develop analytical and explainability frameworks that provide clear visibility into how quality initiatives impact inventory supply, advertiser spend, and marketplace dynamics.
• Cross-Functional Impact
• Collaborate with product and business stakeholders to translate analytical insights into clear, defensible decisions and marketplace enforcement strategies.
• Partner closely with engineering to deploy scalable, performant models.
• Anticipate emerging marketplace risks and proactively develop data-driven solutions to protect marketplace integrity.
• Leadership & Mentorship
• Act as a staff-level technical leader, setting standards for analytical rigor, model evaluation, and scientific best practices across MQE.
• Mentor and develop data scientists through design reviews, technical guidance, and career coaching.
• Influence technical direction across teams by driving alignment, knowledge sharing, and a culture of high-quality decision-making.
• WHO WE ARE LOOKING FOR
• Advanced degree (MS or PhD) in a quantitative field such as Statistics, Computer Science, Economics, Applied Math, Operations Research, or similar.
• 7+ years of experience working in a DS role that involves bringing products from ideation to production.
• Deep expertise in statistical modeling, machine learning, and large-scale data analysis.
• Strong understanding of experimentation, causal inference, and metric design in complex systems.
• Familiarity with large-scale data and ML tooling (e.g. Spark, distributed training, real-time inference systems).
• Proven ability to lead ambiguous, high-impact projects end-to-end as a senior individual contributor.
• Excellent communication skills and the ability to influence across organizational boundaries.
• Experience in programmatic advertising and/or real-time auctions is a plus.
• Track record of mentoring senior data scientists and shaping team-level technical direction is a plus.

Benefits

Additional Information

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. We are looking for a Lead Staff Data Scientist to join the Marketplace Quality Engineering (MQE) team. MQE is a cross-functional team responsible for the integrity, performance, and trustworthiness of The Trade Desk's inventory marketplace. We build systems that detect and prevent fraud, measure and improve inventory quality, and optimize marketplace dynamics to deliver better outcomes for advertisers, publishers, and consumers. In this role, you will provide technical and strategic leadership within MQE by defining data science roadmap and execution plans, owning end-to-end development of advanced modeling, analytical, and measurement systems, and partnering closely with engineering, product, and business leaders to translate complex requirements into trusted, scalable solutions.

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Company Intel

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