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DoorDash Ads Economics team is responsible for maintaining a healthy ads marketplace across all verticals for both search and discovery experiences. This role will drive the design and development of large-scale ML/optimization systems within the Ads Delivery funnel to improve advertiser ROI and marketplace efficiency.

Requirements

• 8+ years of industry experience building production-scale ML systems.
• Expertise in at least one of: auction design, pacing, bidding optimization, or forecasting.
• Strong understanding of probability theory, statistics, and machine learning fundamentals.
• Proven ability to lead cross-functional initiatives and drive complex technical projects end-to-end.
• Experience in Ads or marketplace systems is a plus

Responsibilities

• Lead the technical design and implementation of ML solutions for key Ads Economics areas such as Auction, budget pacing, bidding optimization, and forecasting.
• Partner closely with Product, Data Science, and Engineering teams to design experiments, model frameworks, and production ML systems that directly impact advertiser ROI and marketplace efficiency.
• Build and deploy 0→1 ML systems that improve ad delivery outcomes and marketplace health.
• Set best practices for model training, evaluation, deployment, and monitoring
• Act as a technical thought leader for Ads ML — influencing roadmap, experimentation design, and overall Ads system evolution.
• Own impactful ML systems: Build and improve models that directly have a large impact on top and bottom line financials.
• Drive experimentation: Rapidly test hypotheses via robust sequential experiments; measure and explain your models’ impact on marketplace KPIs

Other

• Provide technical mentorship and guidance to engineers and cross-functional partners — leading through influence, not management.
• Collaborate cross-functionally: Partner with engineering, analytics, product, and operations to iterate quickly, moving models from prototype to production
• Excellent communication skills — able to explain technical concepts to product, business, and engineering audiences.
• M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or a related field.