Senior Applied Scientist, DemandTech: Autonomous Ads

Amazon; New York (NY); On-site; USD 150,000 - 230,000; Full time

Job summary

Amazon is seeking a Senior Applied Scientist to lead the science behind the next-gen smart ads campaign. You will own the Model Layer, turning threshold-based logic into self-improving intelligence and define decision policies for autonomous actions that advertisers can trust.

You will build predictive models, establish causal attribution, and integrate signals across creativity, relevance, and budget to optimize campaigns while collaborating with engineers to deploy these models in production.

Responsibilities

• Build predictive issue-detection models that identify under-delivery, over-delivery, and performance degradation from campaign signals before they materially impact advertisers.
• Design the intervention-selection policy — which autonomous action to take — framed as a contextual bandit: choose, observe, update.
• Establish causal attribution for autonomous optimization, separating the effect of our interventions from organic performance change, so improvements can be attributed and advertiser trust in autonomy can be earned.
• Integrate and adapt cross-model signals. Combine creative-quality, product-relevance, and budget signals into a unified advertiser-intelligence picture, and adapt general-purpose partner models to our product reality via fine-tuning, re-ranking, or thin adaptation layers.
• Close recommendation and grading feedback loops — define reward schemas for accept/reject and performance-vs-baseline signals, correlate creative-quality scores with real campaign outcomes, and feed empirical findings back to both our product and partner science teams.

Job description

Amazon is seeking a Senior Applied Scientist to lead the science behind the next-gen smart ads campaign. You will own the Model Layer, turning threshold-based logic into self-improving intelligence and define decision policies for autonomous actions that advertisers can trust.

You will build predictive models, establish causal attribution, and integrate signals across creativity, relevance, and budget to optimize campaigns while collaborating with engineers to deploy these models in production.