Principal Data Scientist, Analytics at LiveRamp in San Francisco, CA

Job description

LiveRamp is shaping the future of responsible data collaboration between the world’s leading brands, retailers, financial services providers, and healthcare innovators. As consumers embrace new AI-driven experiences, the LiveRamp data collaboration network exponentially expands the breadth and accuracy of the data on which marketing AI capabilities operate, powering deeper customer insight and measurable performance on a global scale. LiveRamp is headquartered in San Francisco, California, with offices worldwide. Learn more at LiveRamp.com . Job Description Role Summary As we scale our self-service analytics and DS capabilities, we’re looking for a Principal Data Scientist, Analytics to develop scalable data & analytics solutions and framework, and intuitive tools that bring insights directly to business teams. The Principal Data Scientist, Analytics plays a critical role in delivering trusted, scalable analytics solutions to stakeholders across the company, particularly in the Product & Engineering areas. Working closely with architects and data engineers, you’ll help shape data models and pipelines and build Analytics / DS frameworks that serve as the foundation for high-impact dashboards and predictive and prescriptive analytics. You’ll design user-centric tools that empower teams to explore data, gain insights, and make better decisions, powered by platforms like BigQuery, AI agents, and Tableau. Key Responsibilities Self Service Analytics & Insights Design, build, and certify reusable, self-service metrics, dashboards, skills and agents, and analytical products based on business needs for stakeholders in Product and Engineering teams to independently answer complex questions and provide actionable insights. Partner with Product and Engineering to align strategic goals and embed analytics and decision logic directly into the product lifecycle. Create semantic layers, metric frameworks, and analytical abstractions that power AI-assisted insights and natural language querying. Develop complex analytical models, provide "so-what" deep dive analyses, and present findings to leadership for high-impact use cases. Own explainability, trust, and governance for AI-driven analytics experiences. Data Science, Modeling & Experimentation Develop predictive, diagnostic, and causal models to understand and optimize product adoption, engagement, retention, and monetization. Translate ambiguous product questions into formal models and statistically sound analyses. Perform scenario modeling and simulation to inform roadmap and investment decisions. Own experimentation strategy, including A/B testing, quasi-experiments, and causal inference. Partner with data engineering to architect data stack, build model-ready datasets and scalable feature pipelines, and embed data operations for reproducible data science models. Validate models, create framework, review analytical rigor, and raise the quality bar across product analytics and data science work. Leadership Partner with Product, Engineering, and Design teams to define and prioritize roadmap Mentor and lead cross-functional efforts to evangelize analytical and DS/AI best practices both within and outside the organization. Lead complex cross-functional analytics projects. Required Qualifications MS or PhD in Computer Science, Statistics, Mathematics, or a related field. 10+ years of experience in Data Science and Analytics, with a track record of delivering high-impact product insights and statistical models at scale. Expert-level proficiency in Python and SQL; must be comfortable navigating massive datasets in cloud environments (e.g., BigQuery) and possess hands-on experience building complex DS models and utilizing LLM models. Demonstrated ability to build and scale AI-powered analytics, architect and manage a modern DS stack from the ground up. Deep understanding of Product Analytics metrics and concepts, ideally within a SaaS or Platform environment. Exceptional business acumen with the