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Data Scientist 5 - Advancing Member Measurement
Company: Netflix
Employment Type: Full-Time
Location: 100% Remote – USA
Compensation: $372,000 – $600,000 (USD)
Category: Data/AI/Analytics
Close date or Apply by date: Not Given
WHY THIS ROLE MADE THE CUT
Fully remote (no hybrid or office requirements)
Full-Time role
Job is listed on company site with a direct link to apply
Legitimate, established company
Posted within the last 14 days OR includes a clear deadline to apply
Clear salary range is disclosed
ROLE SNAPSHOT
Netflix is hiring a Data Scientist 5 for Advancing Member Measurement to drive foundational research advancing measurement and understanding of member value with the Member Applied Research Data Science and Engineering team. You'll drive product innovation through robust measurement strategies across experimentation, modeling, and analytics to shape how members experience content types, establish strong partnerships with stakeholders to ladder local developments into holistic measurement approach, contribute new methodologies to causal inference tooling, and develop experimentation and measurement frameworks to increase velocity of investments and aid complex decision-making.
Experience required: Advanced degree in Statistics, Mathematics, Computer Science, Economics, or related quantitative field, 5+ years relevant experience focused on building and delivering real-world machine learning models with demonstrated impact, strong statistical knowledge ideally utilized in experimentation or product analytics settings, and strong quantitative programming skills in Python.
KEY WORDS TO INCLUDE IN YOUR RESUME/COVER LETTER IF YOU APPLY:
Causal inference and experimentation
Machine learning model development
Statistical analysis and product analytics
Python programming
Measurement strategies and frameworks
Cross-functional stakeholder collaboration
Product innovation and decision-making
Advanced degree (Statistics, Mathematics, CS, Economics)
Metric development
Data-driven strategy
Keywords are suggested based on the language used in the employer's job description to help applicants align with automated screening systems.
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