Data Scientist 5 - Games

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

Netflix is seeking a Senior Data Scientist for its Games DSE team to lead experimentation, modeling, and advanced data science initiatives. The role involves partnering with product and cloud gaming stakeholders to drive data-informed decisions by building ML models, conducting statistical analysis, and deriving business insights. The Data Scientist will also lead experiments, develop ML models to enhance player experience, and communicate insights to technical and non-technical stakeholders.

What you'd actually do

  1. Partner with games product and cloud gaming stakeholders (e.g., Games Product Management team, Games Social team, Controller team) on data science and ML initiatives (e.g., ML modeling, causal inference, experimentation etc)
  2. Lead the design, analysis, and interpretation of experiments that shape decision-making around Social, Controller, and Cloud Gaming at Netflix
  3. Develop ML models to enhance player experience and core technology capabilities to enable new Controller, Cloud Gaming and Social experiences
  4. Proactively perform data exploration to identify opportunities for business impact and innovation
  5. Serve as a strategic thought partner to product managers and engineers, directly influencing product direction and improving user experience

Skills

Required

  • PhD degree in Statistics, Computer Science, Econometrics, Mathematics, Engineering or a relevant quantitative field or 8+ years of experience with a Master degree in those fields
  • At least 2 years of experience working with engineering teams as a Data Scientist
  • statistical skills utilized in A/B testing, analyzing observational data, and statistical modeling
  • solid skills with applied ML modeling (e.g., deep neural networks) and assessment
  • comfortable working with large data sets and analyzing complex data with SQL and other tools such as Python or R
  • good business acumen on product innovations and excellent problem solving skills to translate business requirements to data science problems
  • strong bias to action, delivering results quickly with iteration instead of waiting for perfection
  • fast learner and are comfortable with ambiguous requests
  • exceptional communication skills and can manage stakeholder priorities directly
  • Willing to mentor junior data scientists on the team to accelerate team growth

What the JD emphasized

  • ML modeling
  • experimentation
  • statistical analysis
  • applied ML modeling

Other signals

  • ML modeling
  • experimentation
  • statistical analysis
  • business insights
  • player experience