Netflix is looking to improve the creation and optimization of promotional assets by developing models, metrics, and insights to drive technical innovation and produce bottom-line impact on the business.
Requirements
- Expertise in statistical methods, notably regression analysis, forecasting, causal inference, and experimentation
- Knowledge of and experience in applying supervised and unsupervised machine learning methods
- Experience with time-series forecasting and/or financial modeling a plus
- Highly proficient programming in SQL and Python / R
Responsibilities
- Build models to predict the performance of promotional assets, estimate the contribution of those assets to content performance, and quantify the revenue impact from driving member engagement
- Execute and analyze experiments and/or develop causal inference and machine learning models to identify key drivers of positive member outcomes that can be attributed to promotional assets
- Develop innovative statistical, mathematical, and machine learning methodologies to solve novel problems that Netflix faces, and share your work both internally and externally
- Partner with a diverse range of cross-functional business partners to uncover new opportunities and develop metrics and models to guide partners on asset creation and deployment
- Be a technical leader and mentor for other data scientists, analytics engineers and research scientists
Other
- Ph.D. degree (preferred) in a quantitative or computational field such as statistics, computer science, mathematics, economics, finance, engineering, physics, etc.
- 3+ years work experience in relevant data science roles
- 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
