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.
A culture of experimentation enables Netflix to continuously evolve and improve our products, delivering more joy to existing members and attracting new members from around the globe. Because experimentation is so pervasive at Netflix, we continually enable new capabilities and onboard new initiatives to the platform with close cross-functional collaboration with our Data Science partners.
At the nerve center of experimentation at Netflix is our internal, Netflix-wide, Experimentation Platform, responsible for democratizing trustworthy experiments across the company and enabling new capabilities across the company. We are looking to expand the number of full-stack data science faculty on the team to better support experimentation best-practice and drive further innovation in data science tooling.
Responsibilities:
Work closely with data science and engineering stakeholders to act as a strategic thought partner and onboard new workflows.
Design, build, operate, and maintain mathematics and engineering implementations of methodologies employed by the experimentation platform, as well as facilitating data science partner contributions.
Maintain existing inference related data and analysis pipelines, including compute clusters and API services.
Develop new tooling to meet emerging experimentation-related business needs.
Proactively work with the rest of the XP team (including other full-stack data scientists) to ensure that our platform continues to deliver trustworthy and useful analysis results.
Qualifications:
Advanced degree (PhD or Masters) in Computer Science, Statistics, Economics, Applied Mathematics, or related quantitative field
3+ years experience with statistics / inference, ideally in an experimentation context.
3+ years experience with data science languages, ideally including Python and SQL.
3+ years building and operating applications serving internal or external production traffic
Ability to translate mathematical concepts into clean maintainable engineering code.
Curiosity to learn new statistics, methods, optimization techniques, etc.
Excellent communication and collaboration skills.
Able to bring clarity and life to ambiguous problems.
Curious to learn more about our experimentation culture at Netflix? Read up on experimentation culture at Netflix and browse the Netflix Research page.
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.
Inclusionis 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.