Amazon’s Automated Marketing and Experiences (AME) team is building the Internet's largest-scale Search and Social Marketing systems. We are seeking a Senior Applied Science Manager, to lead our Applied Machine learning team that is responsible for a number of algorithms that automatically generate, target, measure, and optimize tens of millions of search engine and social media ad placements that drive a significant portion of Amazon's business.
We are seeking a self-directed leader to set the scientific vision and manage a team senior scientists with a critical mission to influence data science driven solutions across Search and Social Marketing. You will also work with some of the largest external digital advertising partners including technical and product teams from Google, Bing, Facebook, TikTok, Snap and Pinterest to advocate new scientific products and shape industry trends. Ideal candidate will manage the team to develop state of the art machine learning algorithms including generative AI applications for large scale bidding and LLM aided marketing content generation to power Amazon ads. With over a billion product offers and ads worldwide, our programs are some of the largest across Amazon.
Our mission is to engage customers both onsite and off Amazon's websites with the right products and services to enable a great shopping experience. You will go home and show your family and friends why they receive this ad on search or social channels or that email from Amazon. You will make a difference by improving the relevancy for customers and optimizing the investment level for Amazon. Marketing drives a large portion of Amazon’s traffic and business, and represents a unique opportunity to drive impact on the company’s bottom line. state of the art technology and algorithms including statistical modeling, Generative AI, machine learning, and data mining are the core of our business. We also focus on developing novel A/B experimentation mechanisms to measure efficacy of our ML solutions. With essentially full ownership of our own product roadmap, there is a large R&D component to our work, and strong engineering skills together with sound business understanding and an appetite for innovation are highly valued.
Key job responsibilities
- Lead and manage a team of senior Applied, Data and Research scientists, responsible for building large scale ML and GenAI systems.
- You define the strategic vision, a long-term roadmap, establish the right scientific team structure, and lead your team(s) as they deliver high-quality, maintainable analysis, research, and potentially production level models that execute on that vision.
- You work effectively with technical and business leaders. You are able to influence other cross-functional team roadmaps, reach consensus on approach/prioritization, and deliver scientific artifacts and/or products successfully.
- Mentor and guide team members to achieve their career goals and objectives.
- Communicate research findings and progress to senior leadership and stakeholders.
- Rapidly experiment and drive productization to deliver customer impact
A day in the life
As a leader of a central science organization, you will own high visibility programs and influence data science driven solutions across Marketing systems. You will interact with a cross-functional team of science, product, engineering and marketing leaders. This is a highly visible role that requires substantial business acumen as well as technical depth and ability to work with variety of external partners including technical and product teams from Google, Bing, Facebook, TikTok, Snap and Pinterest. If you are passionate about innovation, want to have organization-wide impact, and are looking for opportunities to drive key ML-related programs from idea all the way to production, we look forward to talking to you.
About the team
We are a team of scientists with engineering expertise. We work on prediction, optimization, and experimentation problems to provide data-driven inputs to marketing decisions and build highly scalable machine learning models across Automated Marketing and Experiences (AME) org to drive long-term profitability. Specifically, the team focuses on building re-usable science solutions to address three focal areas: (i) Content selection, creation and moderation via Generative AI, (ii) Bidding which involves valuation, efficiency management and net-profit maximization via elasticity measurement, and (iii) Scalable Experimentation frameworks and statistical techniques for designing and performing causal analysis.
