Principal Analytics & AI Engineer

LiveRamp•San Francisco, CA

1d•$193,500 - $227,500•Hybrid

About The Position

As we scale our self-service analytics and AI capabilities, we’re looking for a Principal Analytics & AI Engineer to develop scalable analytics & AI solutions and frameworks, and intuitive tools that bring insights directly to business teams. The Principal Analytics & AI Engineer plays a critical role in delivering trusted, scalable analytics and AI 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 / AI 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, ML, and Tableau.

Requirements

MS or PhD in Computer Science, Statistics, Mathematics, or a related field.
10+ years of experience in Data Science, Machine Learning, with a track record of building and scaling, and deploying production-grade ML pipelines.
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 both ML models and sophisticated AI agents.
Demonstrated ability to build agent, architect and manage a modern DS/ML/AI 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.
Robust experience implementing MLOps best practices to ensure model reliability and scalability.
A history of leveling up mid-to-senior engineers and driving engineering excellence across the organization.

Responsibilities

AI-Powered Self Service Analytics & Insights: Design, build, and certify reusable AI-powered, self-service metrics, dashboards, agents, and analytical products based on business needs for stakeholders in Product and Engineering teams to independently answer complex questions and provide actionable insights.
Create semantic layers, metric frameworks, and analytical abstractions that power AI-assisted insights and natural language querying.
Partner with Product and Engineering to align strategic goals and embed analytics and decision logic directly into the product lifecycle.
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 MLOps for reproducible data science and ML 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 ML/AI best practices both within and outside the organization.
Lead complex cross-functional analytics projects.