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Analytics Engineer (L5) - Audience & Identity - Ads

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Salary

$100,000 - $720,000

Location

Remote

Our goal in the Audience & Identity Science team is to deepen our understanding of our ad-tier members, in order to improve advertiser performance and ensure a great member experience. We continually improve ad targeting capabilities through machine learning, analytics, data exploration, and optimization. Our work includes segment modeling, expansion algorithms, and lookalike modeling. We also provide modeling and analytics support for Netflix live content and integrations with third-party partners, with a high priority on privacy and data security. We focus on repeatable, scalable modeling for a variety of current and future applications.

As an Analytics Engineer, you will work on defining, tracking, and reporting our key metrics on all Ads audience and identity data products and ML models. You will own multiple key areas, provide insights & reports to both leadership and business stakeholders, and proactively find and pursue projects with high business impact. Ideally, you will be able to present big pictures of complex domains in easy-to-understand formats.

Responsibilities

  • Autonomously identify and pursue research with significant business impact, and make compelling cases for prioritization and resource allocation
  • Proactively perform data exploration on member and advertiser data to discover future opportunities
  • Create prototype data pipelines and partner with data engineers to productize new data sets
  • Identify, compute and validate metrics to measure project success and support product decision making
  • Develop metrics, data audits, dashboards, and ad hoc analyses to support production ML models
  • Deliver well-documented datasets, tools, and reports to key technical and business partners
  • Serve as a strategic thought partner to product managers and business stakeholders, directly influencing product direction and improving user experience.
  • Cultivate strong partnerships with cross-functional stakeholders from product, engineering, operations, design, consumer research, etc.
  • Effectively communicate findings and insights to both technical and non-technical audiences

Qualifications

  • Senior analytics professional with a strong record of data exploration, reporting, data viz (Tableau, D3 etc), and data storytelling skills
  • Expertise in SQL and Python, and some exposure to ETL and data warehousing concepts
  • Experience with production monitoring of machine learning models and high leverage data sets
  • Strong business acumen and ability to translate technical results into business impact
  • Excellent communication and collaboration skills

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