Machine Learning Scientist (l4) - Content & Conversation Modeling

Netflix Netflix · Big Tech · Seattle, WA +2 · Data & Insights

Machine Learning Scientist to develop, optimize, and deploy scalable ML solutions for content strategy, acquisition, scheduling, and advertising at Netflix. The role involves end-to-end ML model development, from ideation to deployment and monitoring, with a focus on informing content decisions and partnering with cross-functional teams.

What you'd actually do

  1. Innovate on a suite of predictive models to help inform our content strategy.
  2. Be a thought partner with our content strategy teams as we continue to evolve our approach to content valuation, scheduling, and performance.
  3. Partner closely with our analytics teams as they leverage our models on the ground both in the US and globally.
  4. Own the end-to-end ML model development lifecycle, from ideation and feature engineering, to model training, evaluation, deployment, monitoring, and continuous improvement.
  5. Inform and influence data and ML infrastructure development through partnership with data engineer, ML Ops, and ML Platform teams.

Skills

Required

  • Python
  • ML/DL framework (e.g., scikit-learn, Keras, PyTorch, TensorFlow, MetaFlow, JAX)
  • Advanced degree (MS or PhD) in Computer Science, Economics, Physics, Statistics, Mathematics, or a related technical field with a focus on machine learning and predictive modeling.
  • Relevant experience in one or more machine learning roles.

Nice to have

  • Appreciation of the creative and entertainment industry

What the JD emphasized

  • delivering business solutions leveraging Machine Learning
  • track-record of delivering business solutions
  • ML model development lifecycle
  • model training
  • evaluation
  • deployment
  • monitoring
  • continuous improvement
  • scaling your ML solutions
  • build modularly
  • resilience

Other signals

  • predictive models
  • content strategy
  • content valuation
  • scheduling
  • performance forecasting
  • ML model development lifecycle
  • deployment
  • monitoring
  • continuous improvement