Applied Science Manager, Customer Intent

- 3+ years of scientists or machine learning engineers management experience
- Knowledge of ML, NLP, Information Retrieval and Analytics
- Experience directly managing scientists or machine learning engineers
- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field, or Master's degree and 4+ years of building machine learning models or developing algorithms for business application experience
- Experience building machine learning models or developing algorithms for business application
- Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers

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The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, WA, Seattle - 183,800.00 - 248,700.00 USD annually
We are seeking an Applied Science Manager to lead the science vision, research strategy, and execution for customer intent modeling that powers next-generation recommendations and personalization. In this role, you will build and mentor a high-performing team of applied scientists, define the multi-year research roadmap, and deliver production-ready models and systems that improve relevance, discovery, and customer trust at scale. The mandate spans modern recommender-system paradigms such as LLM-enabled personalization, intent and journey understanding, representation learning, generative retrieval/ranking, and agentic/conversational experiences grounded in rigorous experimentation and measurable business impact.

Key job responsibilities
Own the scientific vision and roadmap for customer intent modeling across the funnel (browse, search, detail-page engagement, add-to-cart, purchase, and post-purchase), translating ambiguous customer problems into a prioritized research and delivery plan.
Lead and grow a team of applied scientists, including hiring, mentoring, and building a culture of scientific rigor, innovation, and operational excellence.
Drive end-to-end model and system delivery, partnering closely with engineering to design, implement, launch, and operate solutions in high-throughput, low-latency production environments (candidate generation, ranking, re-ranking, and explanation).
Advance state-of-the-art personalization using modern techniques (transformers, LLMs, representation learning, reinforcement learning/bandits where appropriate) and ensure research investments translate into measurable lifts via online experiments.
Establish an evaluation and experimentation strategy for intent and recommendation quality: offline metrics, counterfactual/off-policy evaluation where applicable, calibrated A/B testing, guardrails (trust, safety, fairness).
Build robust intent representations that capture both short-term intent and longer-horizon preferences, with disciplined approaches to privacy, data minimization, and responsible AI
Influence product strategy and executive communication, presenting clear scientific narratives, tradeoffs, and decisions to senior leadership and cross-functional stakeholders (product, design, engineering, privacy/legal).
Raise the scientific bar via external visibility when appropriate: publications, patents, workshops, and internal scientific reviews while balancing novelty with operational impact.