About the job
We are seeking an experienced L5 ML Scientist specialized in forecasting and audience research.
Responsibilities
Build Foundational ML Building Blocks: Develop sophisticated embeddings and models that incorporate deep game-specific signals to solve high-impact business problems, including audience insights, opportunity sizing, and forecasting.
Accelerate Product Development: Build the tools, models, and pipelines required to accelerate DSE workflows across games portfolio, studios, product, and platform.
Bridge the Netflix Ecosystem: Act as a key liaison with the broader Netflix DSE and AI teams to adopt, adapt, and tailor global Netflix capabilities for the unique requirements of the gaming space.
Design Scalable Pipelines: Create end-to-end ML pipelines that accelerate and enable DSE members across games to uncover actionable insights and build data-intensive game features.
Elevate ML Practices: Establish the technical standards for how ML capabilities are applied across game domains.
Qualifications
Minimum
Ph.D. in Computer Science, Machine Learning, or a related quantitative field.
5+ years of experience leading complex, end-to-end ML projects that impact end-customer experiences.
3+ years of experience navigating large-scale technical organizations to align roadmap priorities and share infrastructure
You can digest the latest research paper in the morning and ship a functional prototype or foundational model by the afternoon.
You can bridge the gap between technical ML architecture and business objectives, translating product needs into rigorous technical specifications.
You thrive in zero-to-one environments, enjoying the freedom to choose your stack and define the engineering standards for a new domain.
You have a foundational understanding of causal inference principles, allowing you to discern when a predictive model is sufficient vs. when a causal approach is required.
You have a passion for developing reusable ML capabilities to unlock and accelerate development work broadly.
Preferred
Experience working with game development teams, particularly in game design and engineering.
Experience with building production-grade ML systems, including MLOps best practices.
Have strong engineering skills, particularly in designing and optimizing evaluation frameworks (e.g., Python, PyTorch, LangChain).