Job Title: Data Scientist Company Name: Netflix Job Url: https://explore.jobs.netflix.net/careers/job/790312816943?utm_source=LinkedIn&domain=netflix.com Job Description: 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 goal of our Merchandising and Content Understanding team is to enable operational and creative excellence in the distribution and promotion of our content on our service. We collaborate closely with our partners in the Product Discovery & Promotion organization, and our work directly contributes to launching high-quality content on our service and helps our members discover content they will love. We conduct analyses, build analytical tools, and develop models to help our partners execute on these primary objectives.  We are looking for a talented data scientist to join Merchandising & Content Understanding, which focuses on developing content understanding signals across all formats and improving the discovery experience on our service.  Responsibilities Act as strategic partner for stakeholders and cross-functional collaborators to identify business opportunities and enhance business strategies with novel data science methods in the live event space Define and execute on roadmaps for measuring the impact of content merchandising and improving member experience with Causal Inference and Machine Learning Partner closely with other business leaders, product managers, and other data scientists to refine and scale Causal Inference model based systems Present your research and insights to all levels of the company Become a regional expert on Merchandising and Content Understanding Data Science and Engineering, helping educate and connect with regional offices About you Proven track record of researching and leading Experimentation and Causal Inference methods in ambiguous and complex business areas with a focus on technical rigor and robustness High proficiency in standard tech stack (e.g. Python, SQL), Experimentation (HTEs, multiple hypotheses correction), and common Causal Inference frameworks (e.g., propensity score matching, double machine learning) 4+ years of relevant experience with Experimentation and Causal Inference applications Exceptional communication and collaboration skills coupled with strong business acumen