LiveRamp Ventures invests in companies that help unlock the value of data.
About the role
Define and track success metrics, build experimentation and evaluation frameworks, and tests to quantify the business impact of your work. Contribute high-quality, well-tested, and maintainable code, documentation, and dashboards that make your work reproducible, observable, and easy to operate. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 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
More about this role
LiveRamp is shaping the future of responsible data collaboration between the world’s leading brands, retailers, financial services providers, and healthcare innovators. As consumers embrace new AI-driven experiences, the LiveRamp data collaboration network exponentially expands the breadth and accuracy of the data on which marketing AI capabilities operate, powering deeper customer insight and measurable performance on a global scale. LiveRamp is headquartered in San Francisco, California, with offices worldwide. Learn more at LiveRamp.com .
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,...
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