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Data Scientist (L5) - Content Understanding

icon building Company : Netflix
icon briefcase Job Type : Full Time

Number of Applicants

 : 

000+

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Job Description - Data Scientist (L5) - Content Understanding

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 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

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.

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

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.

Original job Data Scientist (L5) - Content Understanding posted on GrabJobs ©. To flag any issues with this job please use the Report Job button on GrabJobs.
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