The world’s leading streaming entertainment service with 270 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 Content Promotion & Discovery team is responsible for helping members find content they love and growing member engagement.
About the Role
The Content Promotion & Discovery team is responsible for helping members find content they love and growing member engagement. We develop and optimize algorithms and product experiences across many surfaces, including the Homepage, search results, and email notifications. Our goal is to create a seamless and personalized discovery experience for every member.
Responsibilities:
- Design and analyze A/B tests and other experiments to drive product and content strategy in collaboration with cross-functional partners.
- Develop causal inference frameworks to evaluate the impact of content promotion and discovery algorithms.
- Propose, design, implement, and analyze novel algorithms to optimize content promotion and discovery.
- Define and monitor key metrics to track content promotion and discovery performance, identifying opportunities for improvement.
- Communicate insights and recommendations to product, engineering, and content teams, influencing strategy and product roadmaps.
- Collaborate with engineering teams to integrate data science solutions into production systems.
- Mentor and guide junior data scientists on best practices in experimentation, causal inference, and algorithm development.
Prerequisites:
- 8+ years of experience in a Data Scientist role, preferably in the content promotion, discovery, or recommender systems domain.
- PhD or MS in a quantitative field (e.g., Statistics, Computer Science, Economics, Operations Research, Engineering) with 8+ years of industry experience, or BA/BS with 10+ years of experience.
- Proficiency in causal inference techniques (e.g., difference-in-differences, synthetic control, instrumental variables) and experimental design (A/B testing).
- Strong programming skills in Python or R for data manipulation, statistical modeling, and machine learning.
- Expertise in SQL for data extraction and analysis.
- Experience with big data technologies (e.g., Spark, Hadoop) and cloud platforms (e.g., AWS, GCP, Azure).
- Demonstrated ability to translate complex data into actionable insights and communicate them effectively to technical and non-technical stakeholders.
- Experience leading data science projects from conception to deployment.
- Excellent communication, collaboration, and leadership skills.
