Applied Scientist I, Customer Delivery Excellence Science

Amazon Amazon · Big Tech · Bellevue, WA · Data Science

This role focuses on applying and implementing existing ML solutions to improve global logistics and delivery experiences. The primary responsibility is to build and validate predictive models for delivery time estimation and identify delivery exceptions, directly impacting customer satisfaction and operational efficiency.

What you'd actually do

  1. Build and validate predictive models for delivery time estimation using historical delivery data, weather patterns, and traffic information
  2. Implement models to identify delivery exceptions and risk factors using established ML frameworks
  3. Partner with logistics operations teams to understand business requirements and translate them into modeling approaches
  4. Document model methodologies, assumptions, and limitations for team knowledge sharing
  5. Participate in code reviews and contribute to team best practices

Skills

Required

  • Master's degree in computer science, mathematics, statistics, machine learning or equivalent quantitative field
  • Experience with any programming language such as Python, Java, C++
  • Knowledge of one or more ML Frameworks (e.g., PyTorch, TensorFlow) and ML methods including NLP models (BERT, GPT-2/3), computer vision-based models (object detection, image recognition), and text-based models (Seq2Seq, Topic modeling)
  • Experience in SQL data manipulation
  • Coursework or project experience in statistical modeling, machine learning, or deep learning

Nice to have

  • Ph.D. in computer science, mathematics, statistics, machine learning or equivalent quantitative field
  • Experience with AWS data services (e.g., SageMaker, S3, Redshift, EMR)
  • Experience with distributed computing frameworks (e.g., Spark)
  • Publications at peer-reviewed ML or AI conferences (e.g., NeurIPS, ICML, KDD)
  • Experience with deep learning architecture design and model optimization techniques (e.g., pruning, quantization)
  • Familiarity with A/B testing frameworks and experimentation design
  • Experience in logistics, supply chain, or operations research domains

Other signals

  • improving global logistics
  • data-driven modeling and analysis
  • advanced machine learning and statistical techniques
  • implement proven ML solutions