Senior Data Scientist

LiveRamp•San Francisco, CA

About The Position

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. You will: Design, implement, and iterate on production-grade machine learning and statistical models that power core identity, entity resolution, and measurement capabilities. Analyze and transform large-scale, high-dimensional, and often messy datasets to uncover actionable insights, engineer robust features, and improve model performance and stability. Own end-to-end data science workflows—from problem framing, data exploration, and modeling through deployment, monitoring, and continuous improvement—in close collaboration with Engineering. Translate complex technical concepts and analysis into clear recommendations and narratives for product, engineering, and go-to-market stakeholders to inform roadmaps and prioritization. Define and track success metrics, build experimentation and evaluation frameworks, and tests to quantify the business impact of your work. Partner with Product Management to scope data-driven solutions that address customer needs, validate hypotheses with data, and de-risk new product investments. Contribute high-quality, well-tested, and maintainable code, documentation, and dashboards that make your work reproducible, observable, and easy to operate. Mentor and support other data scientists and analysts through code review s, design sessions, and sharing best practices. Your team will: Build and evolve data science capabilities that sit at the heart of LiveRamp’s identity and data collaboration products, working closely with engineering teams across the company. Tackle a diverse portfolio of problems, from improving core matching and graph-based algorithms to powering customer-facing features for targeting, measurement, and analytics. Collaborate cross-functionally with product, engineering, customer success, and go-to-market teams to ship solutions that are technically sound, operationally scalable, and aligned with customer needs. Maintain a culture of experimentation and scientific rigor, using well-designed tests, strong baselines, and clear metrics to guide decisions. Invest in shared tooling, libraries, and best practices that raise the bar for how data science is done and operationalized across LiveRamp.

Requirements

MS or PhD in Computer Science, Machine Learning, Statistics, Applied Mathematics, or a related quantitative field, or equivalent practical experience.
3+ years of experience designing, building, and deploying data science or machine learning solutions in a production environment.
Proficiency in Python and SQL, along with experience using common data and ML libraries and frameworks (for example, pandas, NumPy, scikit-learn, or similar).
Experience working with large datasets in a cloud environment and with modern data processing frameworks or warehouses (for example, BigQuery).
Demonstrated ability to independently frame ambiguous business or product questions as concrete, testable data science problems.
Strong analytical and problem-solving skills, with a focus on clear measurement, experimentation, and data-informed decision-making.
Excellent written and verbal communication skills, including the ability to present complex technical topics to both technical and non-technical audiences.
A product-focused mindset and a strong bias toward iterative execution—you are comfortable moving from idea to prototype to production quickly while incorporating feedback.

Nice To Haves

Experience with embeddings, representation learning, or large-scale similarity and ranking systems.
Experience with approximate nearest neighbor search, vector databases, or other large-scale vector search technologies.
Experience designing and implementing robust evaluation frameworks and monitoring for ML systems, including offline/online metric alignment and experimentation.
Experience with Google Cloud Platform and its data and ML ecosystem (for example, BigQuery, Dataflow, Vertex AI, or similar).
Familiarity with privacy-preserving data practices and governance, and interest in responsible and ethical use of data.
Experience with identity, entity resolution, or graph-based modeling in advertising, marketing, or adjacent domains.

Responsibilities

Design, implement, and iterate on production-grade machine learning and statistical models that power core identity, entity resolution, and measurement capabilities.
Analyze and transform large-scale, high-dimensional, and often messy datasets to uncover actionable insights, engineer robust features, and improve model performance and stability.
Own end-to-end data science workflows—from problem framing, data exploration, and modeling through deployment, monitoring, and continuous improvement—in close collaboration with Engineering.
Translate complex technical concepts and analysis into clear recommendations and narratives for product, engineering, and go-to-market stakeholders to inform roadmaps and prioritization.
Define and track success metrics, build experimentation and evaluation frameworks, and tests to quantify the business impact of your work.
Partner with Product Management to scope data-driven solutions that address customer needs, validate hypotheses with data, and de-risk new product investments.
Contribute high-quality, well-tested, and maintainable code, documentation, and dashboards that make your work reproducible, observable, and easy to operate.
Mentor and support other data scientists and analysts through code review s, design sessions, and sharing best practices.