Data Scientist L6 - Games Portfolio

Netflix Netflix · Big Tech · United States · Remote · Data & Insights

Senior Data Scientist (L6) at Netflix Games Portfolio Team, focusing on technical leadership for forecasting, metrics, audience insights, and transmedia research. The role involves developing and operating forecasting models, empowering the team, mentoring, and acting as a strategic partner to leadership. Requires expertise in statistical modeling, causal inference, time series analysis, and applied ML, with experience in building and deploying models at scale.

What you'd actually do

  1. Lead the overall technical vision for forecasting, metrics ecosystems, audience insights, and transmedia research.
  2. Own the development and operation of robust and interpretable models for pre-launch and post-launch forecasting.
  3. Empower the team to own metrics ecosystem, audience insights, and transmedia research, enabling the Netflix Games’ objectives as the business grows and evolves.
  4. Serve as the most senior technical resource, mentoring the team and fostering data science excellence.
  5. Act as a technical bridge and strategic thought partner to Games leadership, Finance & Strategy, Engineering, and sister DSE teams, ensuring production-grade data products.

Skills

Required

  • Python
  • distributed systems
  • statistical modeling
  • causal inference
  • time series analysis
  • applied machine learning techniques
  • building and deploying models at large scale
  • defining technical strategy
  • leading large, complex data science projects end-to-end
  • mentoring
  • technical leadership

Nice to have

  • leading the technical definition of core business metrics in the gaming, streaming, or subscription business models
  • building production-grade, large-scale ML systems
  • MLOps best practices
  • solving business challenges with deep neural networks
  • GenAI applications to boost developer productivity

What the JD emphasized

  • technical strategy
  • forecasting
  • metrics ecosystems
  • audience insights
  • transmedia impact frameworks
  • models
  • data pipelines
  • statistical modeling
  • causal inference
  • time series analysis
  • applied machine learning techniques
  • deployed models
  • technical architecture
  • research findings
  • technical and analytical system

Other signals

  • Develop and operate robust and interpretable models for pre-launch and post-launch forecasting.
  • Synthesize deep technical insights into clear, actionable findings for executive and senior leadership.
  • Autonomously identify and pursue high-impact research, making compelling, data-driven cases for resource allocation and prioritization.