Data Scientist (L5) - Ads (Experimentation)

Netflix
USA - Remote$372k – $600kPosted 20 April 2026

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. The Ads Data Science & Engineering team is responsible for the foundational logic of the Netflix ads business. We develop the analyses, tools, and predictive algorithms that drive member joy and advertiser value. As a Data Scientist in this 0–1 space, you will not just execute tasks; you will be a primary architect of our experimentation roadmap, helping us navigate the transition from a nascent offering to a sophisticated global marketplace. Key Responsibilities Scalable Experimentation & Frameworks: Design and execute rigorous experimental frameworks. You will lead the transition from manual analysis to automated, scalable solutions integrated with the Netflix Experimentation Platform, defining best practices that ensure trustworthy decision-making at scale. Marketplace Dynamics & Auction Theory: Drive the advancement and implementation of biddable media models and dynamically priced auctions. You will develop strategies to optimize marketplace mechanics, ensuring balance between supply, demand, and member experience. Global Expansion & Scaling: Partner with Product and Engineering to solve "cold-start" measurement challenges and rapidly scale our ad platform across diverse international markets. Strategic Thought Leadership: Act as a high-level consultant to Product, Strategy, and Engineering teams. You will autonomously identify research opportunities, quantify their potential business impact, and advocate for resource allocation to pursue them. Collaborative Partner: Cultivate strong partnerships with cross-functional stakeholders including product, engineering, operations, design, and consumer research. Technical Excellence: Deliver end-to-end solutions using advanced causal inference, machine learning, and data exploration, maintaining a high bar for documentation and reproducibility. Requirements Advanced Quantitative Background: MS or PhD in a quantitative field (e.g., Statistics, Economics, Mathematics) or equivalent practical experience. Specialized Domain Expertise: Significant experience with auction dynamics, yield management, or supply-demand matching within a marketplace setting. Statistical Rigor: Mastery of causal inference and experimental design, with specific experience solving for interference and network effects in marketplace environments. Technical Stack: Expert proficiency in Python or R, and advanced SQL. Strategic Communication: Ability to translate complex statistical results into actionable business narratives for stakeholders at all levels of the organization. Netflix Culture: A self-starter who thrives in an environment of radical transparency and high autonomy. You are a mentor and a collaborator who prioritizes the inclusion of diverse perspectives to reach the best possible decisions. 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 Injury Benefits. We also offer paid leave of absence ... (truncated, view full listing at source)
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