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
Comfortable with ambiguity; able to take ownership, and thrive with minimal oversight and process
Netflix culture resonates with you
Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off. See more details about our Benefits here.
Netflix is a unique culture and environment. Learn more here.
Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner.
We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.
Job is open for no less than 7 days and will be removed when the position is filled.
Top Skills
What We Do
Netflix is the world's leading streaming entertainment service with 209 million paid memberships in over 190 countries enjoying TV series, documentaries and feature films across a wide variety of genres and languages. Members can watch as much as they want, anytime, anywhere, on any internet-connected screen. Members can play, pause and resume watching, all without commercials or commitments.
