Applied Scientist, Personalization, DTx Science

Saved job description

Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day!

Demand Tech Experience (DTx), as an org, owns the Amazon Ads Console — the advertiser-facing workspace for creating and managing campaigns, reviewing performance, and acting on recommendations used by millions of advertisers. Our team, DTx Science, is the centralized science team within DTx, dedicated to improving and protecting the advertiser experience across ad consoles through applied science methodologies. The team drives impact through four core functions: Ad Console Personalization, Recommendation Development, Ad Console Experimentation, and DVA Agent Evaluation and Impact Measurement. We leverage a breadth of state-of-the-art techniques such as deep learning, reinforcement learning, LLMs, long term causal modeling as well as sophisticated A/B testing to develop personalized experiences with directly quantifiable business and advertiser success outcomes. We are looking for an accomplished machine learning expert to lead the Applied Science strategy for our recommendation creation and personalization program.

In this role, you will work closely with business leaders, stakeholders and cross-functional teams to drive program success through ML-driven solutions. You will shape the applied science roadmap, promote a culture of data-driven decision-making, and deliver significant business impact for millions of advertisers worldwide and the company using advanced data techniques and applied science methodologies.

Key job responsibilities

As an Applied Scientist on this team, you will

- Serve as the technical leader in Machine Learning, driving collaboration between scientists and engineers, guiding efforts within the team and collaborating with other teams.

- Conduct hands-on analysis and modeling of large-scale data to generate insights that boost ads revenue while maintaining a positive advertiser and shopper experience.

- Lead end-to-end Machine Learning projects that involve high levels of ambiguity, scale, and complexity.

- Build, experiment, optimize, and deploy machine learning models, collaborating with software engineers to bring your models into production.

- Run A/B experiments, gather data, and perform statistical analysis to validate your models.

- Develop scalable and automated processes for large-scale data analysis, model development, validation, and serving.

- Explore and research innovative machine learning approaches to push the boundaries of what’s possible.

About the team

The DTx Science team is the centralized science team within Demand Tech Experience (DTx), dedicated to improving and protecting the advertiser experience across ad consoles through applied science methodologies. The team drives impact through four core functions: Ad Console Personalization, Recommendation Development, Ad Console Experimentation, and DVA Agent Evaluation and Impact Measurement.

Improve advertiser experience: Ad Console Personalization and Recommendation Development aim to improve the advertiser ad console experience by providing customized and personalized console experiences for each user and increasing their ad performance with relevant, science-backed onboarding and oppertunty recommendations.

Protect advertiser experience: Ad Console Experimentation and DVA Agent Evaluation and Impact Measurement protect the advertiser experience by supporting the organization in making data-driven decisions to launch UX and agentic features that benefit advertisers.

Requirements: - 3+ years of building models for business application experience

- PhD, or Master's degree and 5+ years of CS, CE, ML or related field experience

- Experience programming in Java, C++, Python or related language

- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing