Machine Learning Scientist 4 - Pricing Science

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. The Team Netflix's ability to invest in the content that entertains hundreds of millions of members - billions of dollars each year across film, series, games, and live experiences - depends on getting subscription pricing right. Effective pricing generates the sustainable revenue that funds Netflix's next slate of ambitious content. The Subscription Revenue DSE team brings rigorous measurement and modeling to that challenge. We are a small, focused group of data practitioners who partner with Finance & Strategy, Product, and Consumer Insights to understand how pricing actions actually impact member behavior globally. Our work spans causal measurement of pricing impacts, elasticity, and willingness-to-pay research, and execution analytics. We own and continuously evolve the tools that give Netflix a clear-eyed, evidence-based view of what its pricing decisions actually achieve. The Role We are looking for a Machine Learning Scientist to join our team and bring deep ML and causal inference rigor to some of the hardest quantitative problems in subscription pricing. You will collaborate with other researchers to advance our causal measurement capabilities, own complex ML initiatives end-to-end, and bring deep technical rigor to some of the hardest quantitative problems in subscription pricing. You will also partner with Finance & Strategy leaders and Product managers, translating complex modeling work into clear, actionable insights that drive significant business decisions. What You Will Do Design and implement quasi-experimental and causal inference approaches (difference-in-differences, synthetic control, instrumental variables, and related QED methods) to measure the true impact of pricing actions in observational, global datasets Build and productionize measurement models and causal inference pipelines that estimate how pricing actions affect member behavior - from feature engineering through deployment, monitoring, and iteration Conduct elasticity and willingness-to-pay research to deepen our understanding of member price sensitivity across global markets Evolve our core measurement and analytics tools, integrating new science as the field advances Partner with Finance & Strategy and Product leadership to translate statistical findings - including uncertainty - into business recommendations; push back constructively when business assumptions conflict with statistical evidence Own your work all the way through: from ideation to production systems to learning from real-world outcomes About You You have deep expertise in causal inference and quasi-experimental design - you can distinguish true pricing impact from correlation in messy, global observational data, and you know when findings are conclusive versus when they are not You have a proven track record of taking ML initiatives from 0 to 1, including building, deploying, and maintaining production models You are proficient in Python, with experience in ML and statistical libraries (e.g., scikit-learn, PyTorch, TensorFlow, or JAX) You have experience in B2C subscription businesses and an intuition for how pricing decisions play out at scale You are a clear communicator - you can explain complex causal and ML methodology to non-technical audiences, present uncertainty alongside conclusions, and influence decisions with rigor rather than false confidence You are a first-principles thinker: you identify the right question before choosing the method, and you are naturally skeptical of correlational claims in pricing data You have an advanced degree (MS or PhD) in statistics, economics, co ... (truncated, view full listing at source)