Job description

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. 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. The approximate annual base compensation range is $193,500 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. #LI-AH1 Benefits: People: Work with talented, collaborative, and friendly people who love what they do. Fun: We host in-person and virtual events such as game nights, happy hours, camping trips, and sports leagues. Work/Life Harmony: Flexible paid time off, paid holidays, options for working from home, and paid parental leave. Comprehensive Benefits Package: LiveRamp offers a comprehensive benefits package designed to help you be your best self in your personal and professional lives. Our benefits package offers medical, dental, vision, life and disability, an employee assistance program, voluntary benefits as well as perks programs for your healthy lifestyle, career growth and more. Savings: Our 401K matching plan—1:1 match up to 6% of salary—helps you plan ahead. More about us: LiveRamp’s mission is to connect data in ways that matter, and doing so starts with our people. We know that inspired teams enlist people from a blend of backgrounds and experiences. And we know that individuals do their best when they not only bring their full selves to work but feel like they truly belong. Connecting LiveRampers to new ideas and one another is one of our guiding principles—one that informs how we hire, train, and grow our global team across nine countries and four continents. Click here to learn more about Diversity, Inclusion, & Belonging (DIB) at LiveRamp. LiveRamp is an affirmative action and equal opportunity employer (AA/EOE/W/M/Vet/Disabled) and does not discriminate in recruiting, hiring, training, promotion or other employment of associates or the awarding of subcontracts because of a person s race, color, sex, age, religion, national origin, protected veteran, disability, sexual orientation, gender identity, genetics or other protected status. Qualified applicants with arrest and conviction records will be considered for the position in accordance with the San Francisco Fair Chance Ordinance. We use automated decision systems (ADS) as part of our recruitment and hiring process. If you require an accommodation or believe that the use of an ADS may create a barrier to your application or participation in the hiring process due to a disability or other protected characteristic, please let us know. We are committed to providing reasonable accommodations and ensuring an equitable hiring experience for all candidates. California residents : Please see our California Personnel Privacy Policy for more information regarding how we collect, use, and disclose the personal information you provide during the job application process. To all recruitment agencies : LiveRamp does not accept agency resumes. Please do not forward resumes to our jobs alias, LiveRamp employees or any other company location. LiveRamp is not responsible for any fees related to unsolicited resumes.

Requirements

Must-haves

Self Service Analytics & 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.
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.
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.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

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Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Dental

Vision

401k

Paid Time Off

Parental Leave