Sr Applied Scientist, Private Brands Discovery

Amazon Amazon · Big Tech · CA, BC +1 · Machine Learning Science

This role focuses on designing and building machine learning solutions to enhance customer awareness and product discovery for Amazon's Private Brands. It involves end-to-end project management from ideation to launch, utilizing methods like NLP, deep learning, reinforcement learning, and causal inference. The scientist will work closely with engineering and business teams to solve complex problems and drive measurable business impact.

What you'd actually do

  1. 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.
  2. Drive applied science projects in machine learning end-to-end: from ideation over prototyping to launch. For example, starting from deep scientific thinking about new ways to support customers’ journeys through discovery, you analyze how customers discover, review and purchase Private Brands to innovate marketing and merchandising strategies.
  3. Propose viable ideas to advance models and algorithms, with supporting argument, experiment, and eventually preliminary results.
  4. Invent ways to overcome technical limitations and enable new forms of analyses to drive key technical and business decisions.
  5. Present results, reports, and data insights to both technical and business leadership.

Skills

Required

  • Python
  • ML pipelines
  • deploying models to production
  • monitoring and evaluation
  • causal inference frameworks
  • deep learning
  • machine learning

Nice to have

  • Java
  • C++
  • R
  • scikit-learn
  • Spark MLLib
  • MxNet
  • Tensorflow
  • numpy
  • scipy
  • large scale distributed systems
  • Hadoop
  • Spark

What the JD emphasized

  • building machine learning models for business application experience
  • PhD, or Master's degree and 6+ years of applied research experience
  • Experience with neural deep learning methods and machine learning

Other signals

  • customer awareness
  • drive discovery
  • business impact
  • large-scale problems
  • consumer economy