Data Scientist 6 - Experimentation Platform

Netflix•,

44d•$491,000 - $775,000

About The Position

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. A culture of experimentation enables Netflix to continuously evolve and improve our products, delivering more joy to existing members and attracting new members from around the globe. Because experimentation is so pervasive at Netflix, we continually enable new capabilities and onboard new initiatives to the platform with close cross-functional collaboration with our Data Science partners. At the nerve center of experimentation at Netflix is our internal, Netflix-wide Experimentation Platform (XP), responsible for all experiments across the company. We are looking for a Staff Data Scientist to help set the strategic direction for XP, elevate the rigor of causal inference across the company, and lead the consolidation of a decade of organically-grown, bespoke experimentation practices into a unified, modern platform - all with close cross-functional collaboration with our Data Science and Engineering partners. This is a high-leverage role: rather than supporting a single team's experiments, you will set the standards, tooling, and product direction that every data scientist running an experiment at Netflix depends on.

Requirements

Advanced degree (PhD or Masters) in Computer Science, Statistics, Economics, Applied Mathematics, or a related quantitative field.
8+ years of experience with statistics / causal inference in an experimentation context, including designing experiments at scale and diagnosing them when they break.
Demonstrated track record of setting standards or building tools that were adopted across multiple teams or an entire organization - not just applying judgment project by project.
Deep, practical knowledge of experimentation pitfalls and how to guard against them at a program level: sample ratio mismatches, winner's curse and regression to the mean, false discovery rate across a portfolio of tests, peeking, covariate adjustment, and allocation-vs-analysis-unit mismatches.
Experience translating ambiguous data science pain points into a sequenced product roadmap, including judgment on what a platform should build in, expose as a self-serve primitive, or explicitly not support.
5+ years experience with data science languages, ideally including Python and SQL; comfort partnering closely with engineers on API/schema/system design.
Excellent cross-functional communication skills, with a demonstrated ability to influence skeptical stakeholders - both highly technical (data scientists, engineers) and non-technical business partners to adopt new methods or standards.
Curiosity to learn new statistics, methods, and optimization techniques, and the judgment to know when established methods are the better choice.

Responsibilities

Help set the strategy for the Experimentation Platform, including UI design, and user flows.
Define how data scientists contribute metrics, reports, and templates to the platform so they have high leverage when setting standards for experiments in their own space.
Verify that XP allocates, logs, and processes data using valid, trustworthy causal inference methods, and demonstrate that trustworthiness to partner teams - turning verification into automated, recurring, monitored practice rather than one-off checks.
Act as a strategic thought partner for data science and engineering stakeholders across Netflix.
Bridge the gap between data science requirements and platform engineering implementation, representing the DS organization's needs directly to engineering leadership.
Influence and evolve how Netflix performs experimentation at scale - setting standards for inference practices (e.g. peeking, covariate adjustment, any-time valid methods, metric definitions, allocation mechanisms) and driving adoption of those practices across data science teams that range from long-tenured streaming teams to newer verticals like Ads.
Lead the consolidation of fragmented, bespoke experimentation systems into a coherent, maintainable platform by building the tools and processes that make best practice the path of least resistance.
Mentor and raise the bar for other team members working on or with the platform, and represent XP's point of view in company-wide discussions on experimentation methodology.