Principal Data Scientist - Creator Services

WHY DATA SCIENCE & ANALYTICS?

The Data Science & Analytics organization’s mission is to increase our speed, frequency and acumen of making decisions at scale by instilling a data-influenced approach to building products. We cover a wide area of the data spectrum including analytical data engineering, product analytics, experimentation, causal inference, statistical modeling and machine learning. Aligned and partnering with product verticals, we use this extensive toolbelt to discover new opportunities and unmet use cases, influence and shape the product roadmap and prioritization, build data products and measure impact on our community of players and creators.

WHY CREATOR SERVICES?

The Creator Services team enables unbounded creation through reliable core services, off-platform extensibility, and novel AI applications. This team supports the foundational services that allow creators to build novel experiences to scale to millions of users.

As a Senior/Principal Data Scientist, you will be the primary strategic thought partner to product and engineering leadership for the Creator Services team. You will be responsible for transforming the data culture for this technical, infrastructure-heavy team, moving the pods to increasingly data- and evidence-driven decisions by establishing new analytical frameworks and owning the topline metrics that define success for the team.

You Will:

• Lead the Data-Driven Transformation: Act as the lead data partner to engineering and product VPs, shaping roadmaps with clear success metrics and trade-off analyses. You will establish the rituals (such as metric reviews and deep-dive pre-reads) necessary to instill a data-driven mindset across leadership.


• Define and Own Data Foundation: Build and maintain the metric framework for Creator Services, defining health metrics for various services, and tying them directly to Creator and company-level goals. Act as a core architect of the team’s data ecosystem, partnering with engineers to define essential telemetry requirements and capture new metrics.


• Solve High-Impact Engineering Data Problems: Work deeply with engineering owners on data science projects. Use data to guide critical decisions, such as capacity planning, cost modeling, service pricing, and policy formation. Design analysis for innovative areas and products that pushes the boundaries of Roblox’s capabilities.


• Design experiments and causal studies: Design complex experiments and causal inference frameworks to quantify the impact of product feature launches, foundational service updates, and policy changes to the broader Roblox ecosystem.


• Translate Technical Wins into Business Value: Bridge the gap between deep-tier engineering metrics and high-level business impact. Own the narrative that helps stakeholders understand how service improvement drives creator success and platform sustainability.

You Have:

• A BS, A MSc or PhD in Statistics, Economics, Computer Science, Applied Math, or a related quantitative field, or equivalent degree and/or practical experience.


• Expert-level knowledge of A/B testing, statistics, and causal inference in complex, mediated settings.


• A demonstrated ability to influence senior engineering and product leaders through clear narratives, decision memos, and live reviews - especially on complex, technical topics like limits, safety, and economics.


• The ability to quickly grasp complex engineering contexts, including system architecture, data flows, and schemas behind high-throughput services.


• Experience developing roadmaps in nascent areas and the ability to pivot focus based on shifting company priorities and team innovations.


• Comfort with Ambiguity: You thrive in "zero-to-one" environments where data may be new, and the path forward is not predefined. You have a proven ability to bring structure to chaotic problem spaces and define the analytical playbook where none exists.


• Deep Infrastructure Domain Knowledge: Proven track record modeling serving costs, storage growth, and capacity limits, and partnering with engineering to make architecture and efficiency decisions.