Machine Learning Scientist 4
Job description
Machine Learning Scientist 4 at Netflix.
About the role
This position focuses on building predictive models that guide content acquisition, scheduling, and advertising decisions. You will work within a specialized group to turn complex data into actionable insights for the global entertainment catalog.
Key facts
What you'll do
- Design and refine predictive models to support content strategy.
- Collaborate with strategy teams to improve methods for valuing and scheduling content.
- Work with analytics groups to apply model outputs to real-world scenarios.
- Manage the full lifecycle of ML models, including feature engineering, training, deployment, and ongoing monitoring.
- Coordinate with data engineering and ML Platform teams to improve infrastructure and tooling.
Requirements
- Advanced degree (MS or PhD) in Computer Science, Statistics, Mathematics, Economics, Physics, or a related technical field with a focus on predictive modeling.
- Significant professional experience in machine learning roles.
- Proficiency in Python and at least one ML/DL framework such as PyTorch, TensorFlow, Keras, scikit-learn, JAX, or MetaFlow.
- Ability to translate vague business problems into functional technical solutions.
- Strong communication skills with the ability to explain technical details to non-technical partners.
- Experience managing the end-to-end production lifecycle of machine learning systems.
Nice to have
- Interest in the creative and entertainment industry.
Skills & tools
- Python
- scikit-learn
- Keras
- PyTorch
- TensorFlow
- MetaFlow
- JAX
- ML lifecycle management
Practical notes
Compensation for this role ranges from $300,000 to $537,000. Netflix uses a salary-only compensation structure where you decide the split between cash salary and stock options annually. Benefits include health, dental, and vision plans, 401(k) matching, disability programs, family-forming support, and life insurance. Salaried employees have access to flexible time off. If you require an accommodation during the interview process, please contact your recruiting partner.