Principal Data Scientist, Analytics
Principal Data Scientist at LiveRamp building scalable, self-service analytics and data science frameworks for Product & Engineering teams — reusable metrics, dashboards, AI-assisted insights, plus predictive/causal modeling and experimentation. Core stack: Python, SQL, BigQuery, dbt, Tableau, and LLMs.
- Compensation
- $193.5K–$227.5K / yr
- Location
- San Francisco, CA
- Work setup
- Hybrid
- Seniority
- Principal
- Employment
- Not specified
- Experience
- 10+ years
- Category
- Data science
- Posted
Skills
Must have
- MS or PhD in Computer Science, Statistics, Mathematics, or related field
- 10+ years of experience in Data Science and Analytics with high-impact product insights and statistical models at scale
- Expert-level Python and SQL; comfortable with massive datasets in cloud environments (e.g., BigQuery)
- Hands-on experience building complex DS models and utilizing LLM models
- Demonstrated ability to build and scale AI-powered analytics and architect/manage a modern DS stack from the ground up
- Deep understanding of Product Analytics metrics and concepts, ideally in SaaS or Platform environment
- Exceptional business acumen; translate vague product questions into technical concepts and communicate impact to executives
- Advanced experience partnering with data engineers and working with dbt or similar data modeling frameworks
- Strong commitment to analytical rigor, reproducibility, and data science best practices
- History of mentoring mid-to-senior analysts and data scientists and driving analytics excellence
Full 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 .
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.