Analytics Engineer 5 - Demand Science

Netflix•Remote, OR

3d•$330,000 - $566,000

About The Position

The Demand Science team within Ads Data Science & Engineering plays a crucial role in Netflix Ads growth by equipping the business to efficiently attract, activate, and scale advertiser demand. As the dedicated analytics engineer in this space, you would lead analytics across our media planning and campaign management tools, ML models, and agentic solutions end-to-end. You will be responsible for leading the space; finding the patterns, risks, and opportunities before anyone asks for them, and turning that into a point of view that changes what the team builds next.

Requirements

5+ years in a data analytics or data science function, with a track record of independent, open-ended analysis, in addition to dashboard delivery.
5+ years of experience in leveraging technical skills in manipulating large data sets with complex SQL and Python (or similar languages), big data technologies (e.g., Hadoop, Spark), and visualization tools
Comfort with ambiguity; able to take ownership, and thrive with minimal oversight and process
Track record of turning analysis into a point of view that changes roadmap or product decisions.
Experience managing stakeholders' asks, expectations, and relationships across a variety of stakeholders
Exceptional communication and collaboration skills coupled with strong business curiosity and acumen
Enthusiastic about and compatible with Netflix culture.

Nice To Haves

Experience with LLM techniques that accelerate data analysis.

Responsibilities

Partner directly with stakeholders (e.g., Scientists, Product Managers, Engineers) on media planning and campaign management analytics initiatives.
Lead open-ended discovery, surfacing risks and opportunities before they're asked for.
Own end-to-end development of the analysis, metrics, and tools used across Ads Demand.
Turn data into insights and recommendations that shape campaign and planning outcomes.
Define and evolve the metrics that describe campaign and planning health.
Partner with data engineering to keep analytics data reliable and available.
Communicate findings and recommendations clearly to technical and non-technical audiences.