Data Scientist III

Chewy Chewy · Retail · Bellevue, WA

Data Scientist III role focused on developing and deploying machine learning infrastructure, data pipelines, and algorithms to improve data quality, optimize products, and drive business metrics. Requires experience across the data science lifecycle, proficiency in programming languages and ML frameworks, and cloud platform experience.

What you'd actually do

  1. Develop machine learning infrastructure and data pipelines that improve data quality.
  2. Use machine learning frameworks in both development, testing, and production environments to create and deploy new technologies.
  3. Identify opportunities for data science to improve current products and practices for business engineering teams.
  4. Create machine learning algorithms to optimize and deliver results by reducing computational complexity, increasing the accuracy of models, and improving business metrics.
  5. Perform data ET, statistical and analytical analyses, and communicate insights and recommendations to Chewy management to make informed decisions.

Skills

Required

  • Master's degree in Data Science, Analytics, Engineering, or related field and 4 years of experience as a Data Scientists or related position/occupation.
  • Ph.D. degree in Data Science, Analytics, Engineering, or related field and 2 years of experience as a Data Scientists or related position/occupation.
  • At least one data science subject area (e.g., casual inference, NLP, forecasting, etc.)
  • R
  • PySpark
  • Spark
  • Scala
  • Java
  • PyTorch
  • TensorFlow
  • Docker
  • Object-oriented programming with Python
  • Data visualization tools and packages (Tableau or similar)
  • Managing the entire data science lifecycle including data prep, exploratory data analysis, modeling, interface with engineering
  • Amazon Web Services tools such as Redshift, Snowflake, SageMaker or other similar platforms
  • E-com, retail or start up experience

What the JD emphasized

  • Managing the entire data science lifecycle including data prep, exploratory data analysis, modeling, interface with engineering

Other signals

  • Develop machine learning infrastructure and data pipelines
  • Create and deploy new technologies using machine learning frameworks
  • Create machine learning algorithms to optimize and deliver results
  • Devise models and algorithms and guide engineering to develop scalable solutions