Data Scientist III

Chewy Chewy · Retail · Bellevue, WA

Data Scientist III role focused on developing machine learning infrastructure, data pipelines, and algorithms to improve data quality, optimize products, and reduce computational complexity. The role involves the full data science lifecycle, from data preparation to model deployment, with a focus on improving business metrics and guiding engineering for scalable solutions.

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. Devise models and algorithms and guide engineering to develop scalable solutions that can work in real-time with large amounts of data.

Skills

Required

  • Master’s degree in Statistics, Data Science, Analytics, or related field and 4 years of experience as a Data Scientists or related position/occupation
  • Ph.D. degree in Statistics, Data Science, Analytics, 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, LLM’s, forecasting, etc.)
  • Managing the entire data science lifecycle including data prep, exploratory data analysis, modeling, interface with cross functional stakeholders (such as engineering, business, etc.), deploying models to production
  • Amazon Web Services tools such as Snowflake
  • R, PySpark, Spark, Keras, TensorFlow, Docker, Git version control
  • Object-oriented programming with Python
  • Data visualization tools and packages (Tableau or similar)
  • 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 cross functional stakeholders (such as engineering, business, etc.), deploying models to production

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