Senior Data Scientist - Measurement

Data Scientist

Live Ramp 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, Live Ramp is setting the new standard for building a connected customer view with unmatched clarity and context while protecting precious brand and consumer trust. Live Ramp 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 Live Ramp 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'll own the science and engineering behind Live Ramp's measurement pipeline - the system that our customers rely on to understand the true impact of their media spend. You'll work at the intersection of causal inference, large-scale data engineering, and product delivery, shipping PySpark measurement logic that runs inside privacy-preserving clean rooms on Live Ramp.

You will:

• Use statistical and ML based techniques to reduce selection bias, build representative samples, and analyze randomized studies in the ad-tech space.
• Work hand-in-hand with our engineering team to design and implement models that measure ad performance across large, disparate datasets.
• Perform R&D work by prototyping new statistical and data modeling frameworks, translating prototypes into SQL, pandas-based and Pyspark workflows, and then drive those prototypes into production.
• Extend a well-structured codebase that leverages Python class abstractions and modular pipeline design; write clean, testable measurement logic.
• Troubleshoot or maintain internal models by understanding the key ingredients and underlying assumptions of the models.

Your team will:

• Build and maintain models that deliver in-depth ad campaign measurement inside privacy-preserving clean room environments.
• Translate statistical models into configuration-driven, production-ready PySpark workflows parameterized by different configurations.
• Partner with software and data engineers to design, monitor, and improve the cloud infrastructure that powers end-to-end measurement pipelines across multiple cloud environments.
• Drive new product development in the retail media/brand measurement space - from prototyping new model methodologies to shipping configurable measurement workflows that operations teams can run at scale across multiple customers.
• Engage with internal and external clients to understand their measurement needs and translate those insights into pipeline improvements and new product offerings.

You will:

• Design, develop, and maintain statistical and ML-based measurement models that run at scale in privacy-preserving clean room environments.
• Translate measurement methodology into robust, configuration-driven production pipelines using PySpark and SQL, ensuring correctness through rigorous data quality checks and invariant validation.
• Prototype new statistical and data modeling frameworks - from exploratory research through to production-ready workflows - iterating rapidly while maintaining code quality and reproducibility.
• Apply causal inference and bias correction techniques to address selection bias, build representative national samples, and analyze randomized and observational studies.
• Collaborate closely with data and software engineers to integrate measurement models into cloud-based data infrastructure, and troubleshoot automated pipeline failures end-to-end.
• Partner with product, solutions, and operations teams to translate client measurement requirements into configurable pipeline parameters and new product capabilities.
• Validate and audit model outputs across large, disparate datasets - identifying anomalies, diagnosing root causes, and ensuring results are statistically sound and…