Netflix is one of the world's leading streaming entertainment services, with over 260 million members in over 190 countries enjoying TV series, documentaries, feature films, and mobile games across various genres and languages. Members can play, pause, and resume watching as they want, anytime, anywhere, and change their plans anytime.

　　The goal of our Title & Launch Management Data Science and Engineering team is to enable operational and creative excellence in the distribution and promotion of our content. 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, develop models, and build analytical tools to help our partners execute on these primary objectives.

　　We are looking for a talented machine learning scientist to support the launch of our content and its discovery on our service. You will design and develop models and analyses that will power content launch operations at scale. You will partner with our world-class team of creative production practitioners and various cross-functional teams to shape strategy and deliver impact via machine learning solutions. Interested? Read more about the job description and qualifications below!

　　What you will do:

　　Collaborate closely with stakeholders in Product Discovery & Promotion to learn deeply about content launch operations and identify potentially impactful problems to solve via scalable machine learning solutions

　　Develop innovative systems and models that empower decision-making for stakeholders and product features that can deliver member joy by leveraging a wide variety of metadata and media collected from and generated by our productions throughout their end-to-end lifecycle

　　Collaborate with team members and cross-functional partners to operationalize your models so that they can be integrated seamlessly into operational workflows

　　Serve as a key thought partner for stakeholders, cross-functional partners, and our diverse set of team members regarding machine learning algorithms

　　Your background and characteristics:

　　Ph.D. or MS degree in a quantitative or computational field

　　4+ years of full-time work experience in one or more relevant machine-learning roles

　　Practical and extensive experience in supervised and unsupervised machine learning methods

　　Expertise in statistical analysis methods, most notably regression analysis, statistical inference, and forecasting

　　Proficiency in SQL and programming in Python or R

　　Comfortable and effective in ambiguous problem spaces; ability to own and drive projects with minimal oversight and process

　　Exceptional written and oral communication with technical and non-technical audiences

　　Enthusiasm about and compatibility with Netflix culture (https://jobs.netflix.com/culture)

　　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 $170,000 - $720,000.

　　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 detail about our Benefits here (https://jobs.netflix.com/work-life-philosophy) .

　　Netflix is a unique culture and environment. Learn more here (https://jobs.netflix.com/culture) .