Additional Information
LiveRamp is the data collaboration platform of choice for the world's most innovative companies. A groundbreaking leader in consumer privacy, data ethics, and foundational identity, LiveRamp is setting the new standard for building a connected customer view with unmatched clarity and context while protecting precious brand and consumer trust. LiveRamp offers complete flexibility to collaborate wherever data lives to support the widest range of data collaboration use cases-within organizations, between brands, and across its premier global network of top-quality partners.
Hundreds of global innovators, from iconic consumer brands and tech giants to banks, retailers, and healthcare leaders turn to LiveRamp to build enduring brand and business value by deepening customer engagement and loyalty, activating new partnerships, and maximizing the value of their first-party data while staying on the forefront of rapidly evolving compliance and privacy requirements.
The Principal Analytics & ML Engineer plays a critical role in delivering trusted, scalable analytics and ML 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 ML / 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.
You will:
Architect and manage the modern DS/ML/AI stack for scalable, reproducible data science and ML models.
Build reusable agentic frameworks/pipelines, and lead the development and certification of internal agents to automate analytical workflows.
Develop complex analytical/ML models, provide "so-what" deep dive analyses, and present findings to leadership for high-impact use cases.
Collaborate with data engineering to design/build scalable data models, metrics, dashboards, and analytical products for actionable insights (Product/Engineering stakeholders).
Embed MLOps best practices by implementing monitoring for model drift, version control, and automated retraining pipelines.
Mentor and lead cross-functional efforts to evangelize analytical and ML/AI best practices both within and outside the organization.
Create frameworks/tools for functional analysts to leverage predictive insights without needing data science expertise.
Optimize, refactor, validate, and document business logic, metrics, and data products to ensure proper governance of quality, accuracy, and scalability.
Act as a liaison between business and technical teams to align solutions with strategic goals.
Lead complex cross-functional analytics projects.
About you:
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 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.
The approximate annual base compensation range is $156,500 to $227,500 . The actual offer, reflecting the total compensation package and benefits, will be determined by a number of factors including the applicant's experience, knowledge, skills, and abilities, geography, as well as internal equity among our team.
Liveramp · San Francisco