Applied Scientist, Amazon Private Brands

Amazon Amazon · Big Tech · Seattle, WA · Applied Science

Applied Scientist role focused on designing and building innovative machine learning solutions for Amazon Private Brands discovery, driving customer awareness and product discovery. The role involves end-to-end project ownership, from ideation to launch, utilizing methods like NLP, deep learning, reinforcement learning, and causal inference. The scientist will propose new algorithms, overcome technical limitations, and present findings to leadership.

What you'd actually do

  1. Drive applied science projects in machine learning end-to-end: from ideation over prototyping to launch.
  2. Propose viable ideas to advance models and algorithms, with supporting argument, experiment, and eventually preliminary results.
  3. Invent ways to overcome technical limitations and enable new forms of analyses to drive key technical and business decisions.
  4. Present results, reports, and data insights to both technical and business leadership.
  5. Constructively critique peer research and mentor junior scientists and engineers.

Skills

Required

  • PhD, or Master's degree and 4+ 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
  • Experience in causal ML and treatment effect estimation, including methods like propensity scoring, doubly robust estimators, and uplift modeling.
  • Strong background in Python, ML pipelines, and deploying models to production with robust monitoring and evaluation.
  • Familiarity with causal inference frameworks and translating business questions into actionable causal insights.

Nice to have

  • Experience using Unix/Linux
  • Experience in professional software development
  • Usage of generative AI tools to enhance workflow efficiency, with a willingness to learn effective prompting and evaluation practices.
  • Ability to recognize opportunities where generative AI could enhance products, workflows, or customer experiences.

What the JD emphasized

  • causal ML
  • treatment effect estimation
  • deploying models to production
  • causal inference frameworks
  • business questions into actionable causal insights
  • deep scientific thinking
  • customers discover, review and purchase Private Brands
  • innovate marketing and merchandising strategies
  • advance models and algorithms
  • technical limitations
  • technical and business decisions
  • mentor junior scientists and engineers

Other signals

  • customer awareness
  • drive discovery
  • ML solutions
  • large-scale problems
  • business impact