Senior Applied Machine Learning Scientist - Content Machine Learning
- Los Angeles, California
- Data Science and Engineering
Netflix is revolutionizing the entertainment industry with world-class technology. We are both a content distributor and a producer for original and premium shows. We serve millions of subscribers worldwide in more than 190 countries around the world. We produce hundreds of new series, movies, documentaries, stand-up specials and other categories of content each year. Because of our global footprint, we are able to elevate new types of creators, tell a diverse set of stories and inspire a global audience, transcending geographical and cultural borders.
The Content Demand Modeling team within Science & Analytics plays a central role within the company, informing a wide variety of decision making including how we value content, how best to prioritize marketing spend among titles and which titles to acquire at what price, both from a licensing and original programming perspective. Arming our creative teams with data-driven tools improves both the efficiency and accuracy of decision-making, increasing the odds that we create or acquire content that brings the most relevant stories from all over the world to our current and future members.
The team is currently comprised of a mix of machine learning scientists (ML) and ML engineers, and is responsible for all aspects of owning and innovating upon the suite of ML models that predict content demand -- understanding and deconstructing stakeholder needs and intuition into hypotheses anchored in data, drawing insights from data; evaluating various modeling hypotheses with agility through quick prototyping; pushing the most promising idea to production and ultimately championing the new and innovating solutions to our stakeholders, incorporating their feedback and driving adoption of the models.
We seek to bring on board an experienced machine learning scientist. As a member of this team, you will conduct applied research by investigating, conceptualizing, designing, implementing and validating new algorithms in the area of content modeling.
Examples of Questions you will help to answer (not an exhaustive list):
- Forecasting audience sizes for specific content and how that prediction should inform its valuation and other major business decisions.
- Estimating the acquisition potential of specific content -- what type of content draws audiences to sign up for our service.
- What are the dimensions along which content is cultural or local vs. universal? How can we translate this information into better demand forecasting models for specific content categories, e.g., anime in Japan?
- Opportunity detection -- helping our stakeholders cultivate potential hits in advance and identifying the components that might make them successful.
- How can we understand the mechanics through which various elements that constitute the entertainment space result in its success (or not)?
- In short, our shared mission is to scale decision-making, opportunity detection and discovery for creative exploration.
- Develop machine learning models that will have high-impact on our decision-making.
- Be entrepreneurial and collaborate with business partners (for example, content planning and analysis team) to identify potential high-value applications of machine learning technology to content demand prediction, valuation and opportunity discovery
- Communicate results to a variety of audiences, technical and non-technical.
- Independently deliver effective solutions to problems.
- Own full-stack technology, from data to product and the feedback loop.
- Enact Netflix values in daily work and interactions.
- At least three years of applied ML experience with a successful track record of delivering quality results.
- Solid experience in developing learning methodologies and building robust production machine learning systems.
- Excellent communication skills and an innate ability to translate business context and intuition into data-oriented hypotheses to drive impact.
- Strong coding experience. Experience with open-source ML packages (specifically sklearn, TensorFlow/Keras/PyTorch).
- Desire and willingness to continue growing your capabilities as a ML scientist in innovating modeling and algorithms.
- Passion for and an appreciation of the creative and entertainment industry is definitely a plus.
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