Data Scientist 5 - Personalization Algorithms Performance

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Not verified last checked Jun 13, 2026

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401(k) match

Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked Jun 13, 2026.

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About this role

Data Scientist 5 - Personalization Algorithms Performance USA - Remote At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what's next. We are looking for a talented Senior Data Scientist with strong ML and causal inference experience to lead the analytics, data science, and strategy for our core personalization algorithms that power key member experiences. You will partner closely with product management and engineering teams to shape and execute our personalization roadmap, and will be a key thought partner in defining how we evolve our personalization capabilities powered by advanced ML/AI. In this role, you will: Leverage experimentation, causal inference methods or statistically sound analyses to drive actionable insights and help shape our understanding of how personalization algorithms influence member discovery journeys. Build metrics and measurement frameworks that are statistically and causally robust to empower decision-making for stakeholders and product features that can deliver member joy. Establish strong partnerships with team members and diverse stakeholders across the business. Facilitate ownership and accountability by ensuring that the team is producing trustworthy and high-quality outputs that influence the decisions and direction of a variety of areas of Netflix. Proactively explore trends in content discovery of members to surface future opportunities. Work independently and drive

Read the full description at explore.jobs.netflix.net. FewerJobs shows a source-linked preview and links to the original posting.