Senior Data Scientist - Identity

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 reviews, design sessions, and sharing best practices.

• Build and evolve data science capabilities that sit at the heart of Live Ramp’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 Live Ramp.

• 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, Num Py, scikit-learn, or similar).
• Experience working with large datasets in a cloud environment and with modern data processing frameworks or warehouses (for example, Big Query).
• 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.

• 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, Big Query, 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.
• The approximate annual base compensation range is $130,000 to $196,500.…