Principal Data Scientist, Analytics
About The Position
LiveRamp is seeking a Principal Data Scientist, Analytics to develop scalable data and analytics solutions, frameworks, and intuitive tools that bring insights directly to business teams. This role delivers trusted, scalable analytics solutions to stakeholders across the company, particularly in Product and Engineering. The position works closely with architects and data engineers to shape data models and pipelines and build analytics and data science frameworks that support high-impact dashboards, predictive analytics, and prescriptive analytics. It also designs user-centric tools that help teams explore data, gain insights, and make better decisions using platforms such as BigQuery, AI agents, and Tableau. The role develops self-service metrics, dashboards, skills, agents, analytical products, semantic layers, and metric frameworks; builds predictive, diagnostic, and causal models; and leads experimentation and scenario modeling. It partners with Product, Engineering, Design, and data engineering teams to embed analytics into product development, architect data pipelines and model-ready datasets, and ensure analytical rigor, reproducibility, explainability, trust, and governance. The Principal Data Scientist also leads complex cross-functional initiatives, helps define and prioritize roadmaps, and mentors others while promoting analytics and data science/AI best practices. The approximate annual base compensation range is $193,500 to $227,500. The actual offer depends on factors including experience, knowledge, skills, abilities, geography, and internal equity, and reflects the total compensation package and benefits.
Requirements
- 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.
- Comfort navigating massive datasets in cloud environments, such as BigQuery.
- Hands-on experience building complex data science models and using LLM models.
- Demonstrated ability to build and scale AI-powered analytics and architect and manage a modern data science stack from the ground up.
- Deep understanding of Product Analytics metrics and concepts.
- Exceptional business acumen and ability to translate vague product questions into concrete technical concepts and roadmaps, 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 data science workflow best practices.
- History of developing mid-to-senior analysts and data scientists and driving analytics excellence across an organization.
Nice To Haves
- Experience with Product Analytics in a SaaS or Platform environment is described as ideal.
Responsibilities
- Design, build, and certify reusable self-service metrics, dashboards, skills, agents, and analytical products for Product and Engineering stakeholders.
- Partner with Product and Engineering to align strategic goals and embed analytics and decision logic into the product lifecycle.
- Create semantic layers, metric frameworks, and analytical abstractions for AI-assisted insights and natural language querying.
- Develop complex analytical models and conduct deep-dive analyses; present findings to leadership for high-impact use cases.
- Own explainability, trust, and governance for AI-driven analytics experiences.
- 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 the data stack, build model-ready datasets and scalable feature pipelines, and embed data operations for reproducible data science models.
- Validate models, create frameworks, review analytical rigor, and raise the quality bar across product analytics and data science work.
- Partner with Product, Engineering, and Design teams to define and prioritize the roadmap.
- Mentor and lead cross-functional efforts to promote analytical and data science/AI best practices within and outside the organization.
- Lead complex cross-functional analytics projects.
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Principal
