About the job
The Member Lifecycle and Monetization Data Science & Engineering team plays a critical role for Netflix in driving and accelerating sustainable growth of members and revenue globally, by leveraging data, experimentation & machine learning to develop compelling and persuasive conversion and monetization experiences post-signup to optimize revenue per member. Machine Learning in these areas is a relatively greenfield area, and comes with the potential for 0-1 applications that can drive millions of dollars of impact at Netflix’s scale. We are looking for a research engineer to join the team to contribute to operating, as well as innovating on growth and commerce algorithms in production, validating through running offline experiments, and building online A/B tests to run in production systems. You’ll partner with other ML engineers, scientists and product managers on cross-functional ML initiatives.
Responsibilities
Design, implement and operate high impact machine learning models
Partner closely with cross-functional teams, including researchers, engineers, data scientists, and product managers, to identify high value applications of machine learning, translating business intuition into data-driven solutions
Work closely with scientists and engineers to create scalable, production-ready ML solutions, taking algorithms from initial concept through to deployment in Netflix's large-scale, real-time systems
Contribute to the development of better infrastructure for developing and deploying ML models
Advocate for and apply best practices when it comes to availability, scalability, operational excellence, and cost management
Qualifications
Minimum
A degree in Computer Science or a related field
4+ years of full time engineering experience
Excellent software design and development skills in multi-language settings with Scala, Java, and Python and software engineering best practices (e.g. version control, testing, code review, etc.)
Exceptional communication skills, able to explain complex technical concepts clearly to cross-functional partners
Broad understanding of core machine learning concepts and their application in large-scale, real-world machine-learning systems
Familiarity end-to-end machine learning pipelines (e.g. training or production deployment) and common challenges like explainability
Preferred
No preferred qualifications listed.