Research Scientist II

Chewy Chewy · Retail · Bellevue, WA

Research Scientist II at Chewy focused on developing and implementing models and solutions for complex supply chain problems using optimization, machine learning, statistics, and AI techniques. The role involves analyzing large datasets, collaborating with stakeholders, and implementing process improvements. Requires a Master's or Ph.D. in a quantitative field with experience in ML, optimization, cloud environments, and programming languages like Python and Spark.

What you'd actually do

  1. Develop and implement models and solutions to solve complex supply chain problems using optimization, machine learning, statistics, and relevant simulation or artificial intelligence (AI) techniques.
  2. Deep dive large supply chain data to explain change, develop insights, and provide recommendations.
  3. Work with stakeholders across organizations (with Inbound, Transportation, and Supply Planning) to understand business requirements and translate them into technical specifications for models and algorithms.
  4. Implement process improvement initiatives that drive improvements to the metrics and streamline the supply chain.
  5. Communicate technical concepts and results to non-technical stakeholders in a clear and concise manner.

Skills

Required

  • Master's degree in Industrial Engineering, Operations Research, Statistics, Applied Mathematics, Computer Science, or related field and 2 years of experience as an Operations Research Analysts or related position/occupation.
  • Ph.D. degree in Industrial Engineering, Operations Research, Statistics, Applied Mathematics, Computer Science, or related field.
  • Optimization
  • machine learning
  • data science
  • developing and deploying models in cloud environments like AWS, Azure, Google Cloud, etc.
  • Solving supply chain business problems
  • deep learning models like NNs, CNNs, LSTM, etc.
  • RL models
  • PySpark
  • Spark
  • Docker
  • Object-oriented programming with Python coding including ML libraries
  • Solving optimization problems using Gurobi, Xpress, or CPLEX
  • Use collaborative programming tools (Git, Confluence, etc.)
  • Translate complex data sets and research into simple business recommendations.

What the JD emphasized

  • solve complex supply chain problems
  • develop insights
  • business requirements
  • process improvement initiatives
  • technical concepts and results to non-technical stakeholders

Other signals

  • develop and implement models and solutions
  • solve complex supply chain problems
  • machine learning
  • optimization
  • AI techniques