Amazon Advertising is one of Amazon's fastest growing and most profitable businesses. As a core product offering within our advertising portfolio, Sponsored Products (SP) helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The SP team's primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. 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. We optimize product placements using a combination of machine learning and natural language processing (NLP) algorithms operating in low latency, high-volume systems. We are highly motivated, collaborative and fun-loving, with entrepreneurial drive and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities.
We are seeking an Applied Science Manager who combines technical excellence in machine learning with strong leadership and product intuition, and who has a track record of leading teams of applied scientists and machine learning engineers. This person will be a hands-on technical leader who excels at driving innovation, fostering a data-driven culture, and leading through ambiguity to deliver measurable impact.
In this role, you will:
Lead a team of applied scientists and ML engineers to deliver AI-powered solutions that directly impact product discovery and advertiser success.
Foster a data-driven culture where hypotheses are tested, insights are derived from robust analysis, and decisions are grounded in evidence.
Set technical and scientific direction by defining the vision, roadmap, and success metrics for high-impact ML and AI projects.
Advance scientific excellence by promoting innovation, rigor, and engineering craftsmanship across both science and engineering disciplines.
Drive execution through planning (sprint, quarterly, and annual), goal-setting, stakeholder alignment, and cross-team collaboration.
Provide technical guidance and leadership through ambiguous problem spaces, helping the team navigate experimentation, make sound scientific and engineering decisions, and communicate technical results and business impact effectively to senior leadership.
Mentor and grow talent by hiring top-tier scientists and engineers, and providing ongoing coaching for both career growth and technical development.
