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Data Scientist (L5), TV Innovation

100% Remote Full-time Open now

Netflix is one of the world's leading entertainment services, with over 300 million paid memberships in over 190 countries enjoying TV series, films and games across a wide variety of genres and languages. Members can play, pause and resume watching as much as they want, anytime, anywhere, and can change their plans at any time. The Member Experience Data Science and Engineering team is at the forefront of driving innovations in Netflix’s core product experience within our streaming and new business domains through data. The team collaborates extensively with Product and Engineering teams to identify, incubate and enable product innovations leveraging robust measurement techniques (analytics, experimentation, modeling) and scalable tooling. As a Data Scientist focused on TV innovation, you’ll be shaping the direction of Netflix’s core product, and influencing the member experience for millions of members on our most-watched platform, TV. By partnering with the cross-functional team, you will be driving product innovation while ensuring a coherent overall product experience for our members. In this role, you will:

  • Drive product innovation through robust measurement strategies across experimentation, modeling, and analytics with an overall product vision in mind
  • Apply a member-centric lens to identify pain points and uncover how different member cohorts engage with product interventions
  • Establish strong partnerships with stakeholders to shape the vision of the space, whether that is by helping determine a product strategy or define new metrics
  • Develop experimentation and measurement frameworks to increase the velocity of investments and aid complex decision-making
  • Produce trustworthy and high-quality outputs that influence the decisions and direction of member experience

Visit our culture deck and our Research page to learn about what it’s like to work on Experimentation and Analytics at Netflix. To be successful in this role, you have:

  • Exceptional communication skills with technical and non-technical audiences, able to influence your partners using clear insights and recommendations
  • Exceptional thought partnership, able to own direct relationships with stakeholders and build credibility through clarity and judgment
  • Strong statistical skills and intuition, ideally utilized in experimentation or other product analytics settings, for solving problems in consumer-facing product areas
  • Relevant experience with algorithms as a product (e.g. recommendation systems, ranking algorithms)
  • Expertise in SQL and statistical programming
  • Good judgment to balance between addressing stakeholder or test-specific needs and investing in scalable solutions to serve general use cases
  • Ability to work independently and drive your own projects

Our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is $150,000 - $750,000. 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. Apply Job! Apply to this Job

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