Applied Scientist, Private Brands Discovery

Amazon Amazon · Big Tech · CA, BC +1 · Applied Science

Applied Scientist role focused on designing and building machine learning solutions for customer discovery of Amazon's Private Brands. The role involves end-to-end project management from ideation to launch, with a strong emphasis on causal ML, deep learning, and deploying models to production. The goal is to drive customer awareness and product discovery, impacting Amazon's own brands and contributing to broader discovery solutions across the company.

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
  • causal ML
  • treatment effect estimation
  • propensity scoring
  • doubly robust estimators
  • uplift modeling
  • causal inference frameworks
  • algorithms and data structures
  • data mining
  • parallel and distributed computing
  • high-performance computing
  • Java
  • C++

Nice to have

  • Unix/Linux
  • professional software development
  • Deep learning
  • multi-armed bandits
  • reinforcement learning
  • Bayesian Optimization
  • statistical inference
  • econometrics
  • monitoring and evaluation

What the JD emphasized

  • building models for business application experience
  • patents or publications at top-tier peer-reviewed conferences or journals

Other signals

  • customer discovery
  • machine learning solutions
  • business impact
  • large-scale problems
  • consumer economy
  • marketing and merchandising strategies