Principal Data Scientist, Analytics

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 ability to translate vague product questions into concrete technical concepts and roadmap, and articulate business impact to non-technical executive stakeholders.

• Advanced experience partnering with data engineers and working with dbt or similar data modeling frameworks.

• Strong commitment to analytical rigor, reproducibility, and best practices in data science workflows.

• A history of leveling up mid‑to‑senior analysts and data scientists and driving analytics excellence across the organization.