Applied Scientist, Amazon Ads, Demand Forecasting & Guidance

Amazon Amazon · Big Tech · Palo Alto, CA · Applied Science

The Applied Scientist will lead Machine Learning efforts for Amazon Ads' Demand Forecasting & Guidance team. This role involves hands-on analysis and modeling of large datasets to develop insights, build and deploy ML models into production, and run A/B experiments. The goal is to create best-in-class forecasting products for advertisers to predict campaign outcomes and optimize ad performance, directly impacting key business decisions.

What you'd actually do

  1. Be the technical leader in Machine Learning; lead efforts within this team and across other teams.
  2. Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience.
  3. Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity.
  4. Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models.
  5. Run A/B experiments, gather data, and perform statistical analysis.

Skills

Required

  • building models for business application
  • PhD or Master's degree and 4+ years of CS, CE, ML or related field experience
  • programming in Java, C++, Python or related language
  • algorithms and data structures
  • parsing
  • numerical optimization
  • data mining
  • parallel and distributed computing
  • high-performance computing

Nice to have

  • Unix/Linux
  • professional software development
  • building machine learning models or developing algorithms for business application

What the JD emphasized

  • technical leader in Machine Learning
  • end-to-end Machine Learning projects
  • build machine learning models
  • deploy your models into production
  • machine learning model development
  • machine learning models

Other signals

  • forecasting models
  • machine learning
  • Bayesian Statistics
  • demand forecasting
  • ad-performance predictions