Senior Data Scientist (L5), Games DSE
NetflixUSA - Remote$372k – $600kPosted 4 April 2026
Tech Stack
Job Description
At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next.
Data Science and Engineering (‘DSE’) at Netflix is aimed at using data, analytics, causal inference, machine learning (ML), and sciences to improve various aspects of our business. We are looking for a Senior Data Scientist to join our Games DSE team, leading experimentation, statistical modeling, and advanced data science needs. This role will be partnering closely with our Game studio stakeholders to drive data informed decisions by running experiments, building predictive models, conducting statistical analysis and deriving business insights. As a member of this team, you will also play a critical role in presenting insights that shape our decision making and ensure decisions are sound from a statistical perspective.
What You Will Do:
Partner with our Games Studio stakeholders (e.g., Games Development, Production, Product Management, Tech Lab, Finance and Strategy teams) on advanced data science and modeling initiatives (e.g., prediction modeling, causal inference, experimentation etc).
Lead the design, analysis, and interpretation of experiments that shape decision-making in game verticals and titles.
Advance the audience understanding and product-market-fit for game verticals and titles with data science research.
Proactively perform data exploration to discover innovation and testing opportunities.
Drive and implement high impact projects end to end.
Present your research and insights to key stakeholders.
Work with business stakeholders to connect analysis to key decisions that affect the business to influence and drive impact to the business.
Balance handling ad hoc requests while also driving larger projects forward.
Who You Are:
You have at least 2 years of experience working in the Games industry as a Data Scientist.
At least 5 years of experience with a Ph.D. degree in Statistics, Econometrics, Mathematics, Engineering or a relevant quantitative field or 8+ years of experience with a Master degree in those fields.
You have outstanding statistical skills utilized in A/B testing, analyzing observational data, and statistical modeling.
You are comfortable working with large data sets and analyzing complex data with SQL and other tools such as Python.
You have excellent business acumen on product innovations and excellent problem solving skills to translate business requirements to data science problems.
You have a strong bias to action, delivering results quickly with iteration instead of waiting for perfection.
You are a fast learner and are comfortable with ambiguous requests.
You have exceptional technical and non-technical communication skills and can manage stakeholder priorities directly.
Willing to mentor junior data scientists on the team to accelerate team growth.
You are proactively learning and adopting AI capabilities to supercharge your workflow.
Generally, our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is $372,000.00 - $600,000.00. This compensation range will vary based on location.
Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious In ... (truncated, view full listing at source)
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